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A systematic review of Earthquake Early Warning (EEW) systems based on Artificial Intelligence

• Published: 24 February 2024
• Volume 17 , pages 957–984, ( 2024 )

Cite this article

• Pirhossein Kolivand 1 ,
• Peyman Saberian 2 ,
• Mozhgan Tanhapour 3 ,
• Fereshteh Karimi 4 ,
• Sharareh Rostam Niakan Kalhori 3 ,
• Zohreh Javanmard 3 ,
• Soroush Heydari 3 ,
• Seyed Saeid Hoseini Talari 3 ,
• Seyed Mohsen Laal Mousavi 3 ,
• Maryam Alidadi 5 ,
• Mahnaz Ahmadi 6 &
• Seyed Mohammad Ayyoubzadeh 3 , 4 , 7  

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Early Earthquake Warning (EEW) systems alarm about ongoing earthquakes to reduce their devastating human and financial damages. In complicated tasks like earthquake forecasting, Artificial Intelligence (AI) solutions show promising results. The goal of this review is to investigate the AI-based EEW systems. Web of Science, Scopus, Embase, and PubMed databases were systematically searched from its beginning until April 18, 2023. Studies that used AI algorithms to develop EEWs and forecast earthquake magnitude were qualified. The quality assessment was conducted using the Mixed Methods Assessment Tool version 2018. Detailed analysis was performed on 26 of 2604 retrieved articles. Researchers predict earthquakes most often using neural network family models (21 studies). Among eight categorized groups of parameters for earthquake forecasting, it was often predicted utilizing seismic wave characteristics (65.38%) and seismic activity data (61.54%). AI models most often predicted earthquake magnitude (32.69%) and depth (15.38%). Logistic Model Tree and Bayesian Network had the highest sensitivity, accuracy, and F-measure efficiency (99.9%). Findings showed that AI algorithms can forecast earthquakes. However, additional study is needed to determine the efficacy of more data-driven AI algorithms in mining seismic data using more input variables. This review is helpful for seismologists and researchers developing EEW systems using AI.

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This study has been funded and supported by Iranian Red Crescent Society.

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Department of Health Economics, Faculty of Medicine, Shahed University, Tehran, Iran

Pirhossein Kolivand

Department of Anesthesiology, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran

Peyman Saberian

Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Mozhgan Tanhapour, Sharareh Rostam Niakan Kalhori, Zohreh Javanmard, Soroush Heydari, Seyed Saeid Hoseini Talari, Seyed Mohsen Laal Mousavi & Seyed Mohammad Ayyoubzadeh

Research Center for Emergency and Disaster Resilience, Red Crescent Society of the Islamic Republic of Iran, Tehran, Iran

Fereshteh Karimi & Seyed Mohammad Ayyoubzadeh

Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Maryam Alidadi

Medical Nanotechnology and Tissue Engineering Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Mahnaz Ahmadi

Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran

Seyed Mohammad Ayyoubzadeh

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Seyed Mohammad Ayyoubzadeh conceptualized the research. Pirhossein Kolivand, Sharareh Rostam Niakan Kalhori and Peyman Saberian supervised the manuscript. Mozhgan Tanhapour, Fereshteh Karimi, Zohreh Javanmard, Soroush Heydari, Seyed Saeid Hoseini Talari, Seyed Mohsen Laal Mousavi, and Maryam Alidadi wrote the original draft. Mahnaz Ahmadi reviewed the manuscript.

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Kolivand, P., Saberian, P., Tanhapour, M. et al. A systematic review of Earthquake Early Warning (EEW) systems based on Artificial Intelligence. Earth Sci Inform 17 , 957–984 (2024). https://doi.org/10.1007/s12145-024-01253-2

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Earthquake early warning: what does “seconds before a strong hit” mean?

• Fumiko Tajima   ORCID: orcid.org/0000-0003-1797-9406 1 &
• Takumi Hayashida 2  

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An earthquake early warning (EEW) system is designed to detect an event, determine its parameters (hypocenter, magnitude, and origin time), and issue an alert to sites/areas where necessary actions should be taken before destructive seismic energy arrivals. At present, large-scale EEW systems are operational in several countries around the world. The most extensive nationwide EEW system has been operating in Japan since 2007, and was able to issue alerts broadly when the moment magnitude (Mw) 9 Tohoku-Oki earthquake hit in 2011. The casualties caused by this event were far less than those caused by other deadly earthquakes (Mw  >  6.6) in this century. Many other countries attributed the fewer death victims to the advanced large-scale EEW system, and plan to install systems similar to Japan’s model. However, the historical and environmental background in Japan, both in terms of earthquake hazards and safety preparation, differs considerably from other countries. In addition, EEW systems that use data from a large-scale network (i.e., “a big-net” hereafter) still have limitations. There are thus numerous factors that other countries should consider to benefit from installing a Japan-styled EEW. In this article, we review how research and development associated with EEW have been carried out, and how EEW systems presently function. We then show short-wavelength variation of ground motions within the typical station interval of a big-net using data recorded by a dense local seismic network in Japan. However, it is not particularly meaningful to attempt detailed modeling of varieties of ground motion within the station interval for a big-net EEW operation, because the possible combinations of earthquake sources, paths of wave propagation, and recipient sites are infinite. We emphasize that in all circumstances, for recipients to benefit from EEW, seismic safety preparations must be implemented. Necessary preparations at sites do not diminish in importance after incremental improvements in station coverage and/or algorithms in a big-net operation. Further, scientists and engineers involved in EEW projects should strive to publically disseminate how big-net EEW systems work, and also why, to achieve maximum benefit, these systems should always be supplemented by preparations at recipients’ sites.

Introduction

Earthquakes have caused the worst natural disasters repeatedly. In the twenty-first century alone, there were a number of catastrophic earthquakes that destroyed human lives and environments. Table  1 shows the seven deadliest earthquakes that occurred with casualties of over 10,000 in the twenty-first century. The public wishes that destructive earthquakes could be predicted in advance. There are many networks of seismic instruments on various scales, which were installed in an attempt to observe precursory phenomena of earthquakes around the world. At present, however, it is not possible to forecast the location, time, and size of an earthquake before its occurrence. On the other hand, once an earthquake takes place, a large-scale real-time monitoring network installed to monitor seismic activity in a broad region can detect it, and its earthquake early warning (EEW) system (referred to as a big-net EEW system hereafter) can issue an alert to affected areas through various media seconds before strong ground shaking arrives. At present, there are big-net EEW systems operational in several countries. The system in Japan that the Japan Meteorological Agency (JMA) began operating in 2007 consists of over 4000 contributing stations, with a typical station interval of about 20 km over the country, and has the most extensive record for EEW performance.

End-users/recipients in public may expect to receive an alert from a big-net system before strong ground shaking and so be able to take effective actions. Here, they should be aware of the timeline of receiving an alert and actual ground motion arrival at their sites, which is illustrated relative to an earthquake occurrence in Fig.  1 . Once an earthquake occurs in the area covered by a dense seismograph network, the EEW system detects P-waves from the earthquake and determines preliminary parameters (hypocenter, magnitude, and origin time) using data at three or more stations. If the estimated intensity exceeds a certain level, the system may issue an alert to affected areas (see the explanation of the EEW system; Hoshiba et al. ( 2008 ) and Allen et al. ( 2009 )). It should be noted that the warning time, which is the interval between alert issuance at t A and actual ground motion arrival by S-waves at t S , has to be sufficient for recipients to take effective actions.

Big-net EEW timeline of strong shaking arrival and gray zones for different station intervals. This example is illustrated for an event which occurs at a depth of 10 km. The vertical axis shows the elapsed time (s) from the earthquake origin time, and the horizontal axis the epicentral distance ( D , in km). The travel time curves for P- and S-waves (denoted t P and t S ) are drawn in blue and red as a function of epicentral distance, assuming 5.8 km/s for P-wave speed and 3.4 km/s for S-wave speed for the uppermost layer from the iasp91 model (Kennett and Engdahl 1991 ). A gray zone is the area where strong ground shaking arrives with S-wave at t S before an EEW alert at t A , (i.e., t S   < t A ), and its size depends on the network station interval (Δx) and system processing time. Here, gray zones are shown in different shades for different station spacings, Δx = 10, 20, 30, and 40 km, assuming the system processing time of 2 s after P-wave detection at three or more stations. The maximum D s of the corresponding gray zones are ~ 11 km, ~ 17 km, ~ 23 km, and ~ 29 km, respectively. Outside gray zones, there is a warning time of t S - t A (> 0 s). The further the site is from the epicenter, the longer the warning time is. On the other hand, the ground shaking is stronger and more damage may be caused in the gray zone than outside the zone

However, an alert from a big-net EEW system might not be delivered to areas within a certain epicentral distance ( D ) before strong ground shaking arrives. This is called a “gray zone” in a big-net system. The gray zone size depends on the network station spacing (Δx) and system processing time. Figure  1 illustrates the timeline of strong motion arrival by S-wave as a function of D , and gray zones (shown in different shades) where strong shaking arrives before an alert, for Δx = 10, 20, 30, and 40 km, respectively, assuming an earthquake depth of 10 km and a process time of 2 s. The larger the station spacing (Δx) is, the larger the gray zone is because of a longer event detection time. For example, in the area that is covered by a network of Δx = 20 km, strong ground shaking may start at a location of D  < 17 km before an alert is issued from the EEW system. In the gray zone, the ground shaking is likely to be stronger, causing more damage than outside of it. If the alert is not delivered before a strong hit, it is not useful at the recipient site.

As is seen in Table 1 , the casualties caused by earthquakes in this century are not proportional to magnitudes. The 2010 Mw7 Haiti earthquake caused the most casualties (up to ~ 316,000). In comparison, the total casualties by the 2011 Mw9 Tohoku-Oki earthquake were about 18,500, of which victims of building collapses or landslides due to strong shaking were 4.4% of the total, most of the victims succumbing to tsunamis (Cabinet Office 2011 ). When this earthquake occurred offshore of northeastern Japan (~ 100 km from the coast), the nationwide EEW system detected P-waves ~ 22 s after its rupture onset and issued the initial alert to affected areas in ~ 8 s. Since the system was made available to the public in 2007, this was the first major event, about which many in public broadly received alerts through various media seconds before strong ground shaking.

The JMA EEW system issues an alert when it detects an event with an estimated maximum JMA intensity ( I JMA ) of 5− or larger (see the method for estimating I JMA in Appendix ). During the period from October 2007 to March 10, 2011, there were 17 earthquakes that occurred in the magnitude range between 4.7 and 7.2, for which the EEW system estimated the maximum I JMA at 5− or larger, and issued alerts to affected areas. However, the distributions of estimated I JMA 5− or larger associated with these events were limited only to very local areas. Among the 17 events, there were only 10 events that recorded I JMA 5− or larger by instruments at a few stations (The data files were downloaded from http://www.data.jma.go.jp/svd/eew/data/nc/rireki/rireki.html to compare the observed intensities with the estimated ones. This site is in Japanese only). In the 2011 Tohoku-Oki earthquake sequence, the system did not issue an alert for the Mw7.3 foreshock of March 9th, because the maximum I JMA estimated in the real-time scheme was below 5−. For the mainshock (Mw9) on March 11th, the system estimated I JMA of 5− and larger for a much broader region than the areas estimated for the previous 17 earthquakes.

Strong ground shaking was felt even in the crowded Tokyo metropolitan area (equivalent to I JMA over 5+), which is more than 300 km away from the epicenter. The number of casualties caused by the quake was not great compared to other similar size earthquakes, such as the 2004 Indian Ocean earthquake, despite the very high levels of ground acceleration (i.e., peak accelerations of over 2 g at some sites; Furumura et al. ( 2011 )) owing to high-standard building codes and advanced engineering preparations (see Tajima et al. ( 2013 ) for a review). Here note that, since it was enacted in 1950, the Building Standard Law of Japan has been updated every time after a major earthquake has occurred. Regarding Japan’s earthquake-resistant standard, a major amendment was made in 1981 (called as “New Earthquake Resistance Standard”) while further revisions for wooden houses were made in 2000, based on damage analyses of the 1995 Mw6.8 Great Hanshin (or Kobe) earthquake.

Many other countries attributed the fewer fatalities of the Mw9 Tohoku-Oki earthquake largely to the extensive nationwide EEW system, and now plan to install dense real-time monitoring and EEW systems of their own. There have been a number of symposiums and workshops on research and developments (R&D) of EEW including an international session “Earthquake early warning developments around the world” held at the 2016 Japan Geoscience Union (JpGU) meeting jointly with the American Geophysical Union (AGU) (“the 2016 JpGU-AGU joint session on EEW” hereafter; see http://www.jpgu.org/meeting_e2016/session_list/detail/S-SS01.html ) to promote communication in this endeavor among the countries. Most of the presentations at the workshops/symposiums focused on improvements of station coverage and methods of data analyses for big-net EEW systems.

However, EEW systems are useful only if the strength of structures and resilience of operations are secured at individual sites against strong shaking. We call this “site-effective preparations” for seismic safety. Figure  2 illustrates an EEW alert delivery to areas which may be struck by strong shaking. People may be able to protect themselves in seconds. Power may be turned off to shut down operations. Buildings may not collapse if they were built with a high-standard building code or retrofitted suitably. This illustration also shows that a strong quake may hit a high-tech facility and cause serious damage to the machinery if there is no seismic isolation system beneath the building. In the case of the 2011 Tohoku-Oki earthquake, preparations for seismic safety at individual sites and the operation of the extensive EEW system were most instrumental in mitigating the shaking damage. As stated above, the earthquake-resistant standard was majorly updated in 1981 and 2000. New buildings were built with the updated building standard while old buildings were retrofitted accordingly. Site-effective preparation is absolutely necessary for EEW. Without such preparations, expectations of EEW by the public would be similar to those of earthquake predictions in the past.

Schematic illustration of responses to an EEW alert including a hypothetical high-tech facility. Upon receipt of an alert, the power may be shut down. People may be able to protect themselves and buildings may not collapse if they were built or retrofitted with a high-standard building code. However, at a high-tech facility, without a seismic isolation system beneath it, a strong shaking could directly hit vulnerable machinery and cause serious damage to it. For example, machinery in operation under high-voltage and high currents may not stop operating instantaneously after the power is turned off

We (the authors of this paper) participated in the beta-phase test of the EEW system in Japan before it became publically operational in 2007, and in the beta-phase test of California Integrated Seismic Network (CISN) in the western United States since 2013 (see the real-time networks for EEW systems in Japan and the western United States in Fig.  3a, b ). It has been more than a decade since public operation started in Japan while it is still in a test phase and not available to the public in the western United States. In this review, we briefly summarize how EEW systems have been developed, focusing on the developments of observation networks in Japan and the advanced abilities and limitations of EEW technologies at present. We then summarize issues pointed out during the system beta-phase testing and workshops/symposiums to address “What can we do in seconds to respond to an EEW alarm before strong shaking?” We address the knowledge gap between the advanced EEW technology and its application to improve seismic safety preparation at the sites of alert recipients, i.e., site-effective preparation. The scope of this paper is not to summarize the detailed system developments and testing, which were reviewed by other researchers (e.g., Strauss and Allen 2016 ; Cochran et al. 2017 ).

Station distribution for EEW operation: a the Japanese network stations by JMA, NIED (Hi-net and part of KiK-net), and JAMSTEC (DONET) (> 4000). Here, 30 KiK-net borehole station data around the Kanto region are included (Iwakiri et al. 2012 ); b the network stations by CISN, PNSN, and others that contribute to ShakeAlert in the West Coast region/USA (in beta-test phase with ~ 830 stations, as of July 2017)

The desire for earthquake hazard mitigation stimulated earthquake prediction research in the past and the development of earthquake monitoring networks attempting to detect precursory phenomena. We consider it as leading to the recent development of real-time EEW systems, especially after the 1995 Kobe earthquake disaster in Japan. Now, real-time seismology and applications of EEW technology have shown remarkable advances in hazard mitigation efforts (see Kanamori et al. 1997 ; Kanamori 2008 ).

From earthquake prediction to real-time monitoring

We used to think that if an earthquake were predicted , earthquake hazards could be mitigated . Then, we asked ourselves: “How shall we respond to an earthquake prediction if it is announced minutes, hours or days in advance?” At present, it is not possible to predict an earthquake with accurate parameters (location, occurrence time, and size) before it occurs. But we have achieved the stage where we can receive an alert from an EEW system seconds before a strong quake hits a site, and explore how to mitigate earthquake damage using the information. We explore: what can we do effectively in seconds before strong ground shaking ? The concept of EEW is prevalent in public now. But the public is not necessarily aware of the reality of the information service that a big-net EEW offers, and simply transferring their past expectations from earthquake prediction to big-net EEWs.

Expectations toward earthquake prediction and seismograph network installations

The history of attempting to predict earthquakes is long. In modern times, the Japanese government has started a major national project to promote earthquake prediction research, the Earthquake Prediction Plan (EPP) in 1965. EPP aimed to detect foreshocks, precursory deformation, or any phenomena associated with an impending earthquake, and installed many seismic instruments over the country. During the EPP period, there was no data recorded for precursors to lead to an earthquake prediction. But EPP continued up to its seventh term ending in 1998.

During the Japanese EPP period, there was a successful evacuation prior to a large earthquake in Haicheng, China in 1975. This Mw7.3 earthquake was preceded by precursors, i.e., many small earthquakes, a change in groundwater levels, abnormal animal behaviors, etc. (e.g., Adams 1976 ). The regional government determined that a large earthquake was about to occur and, the morning of February 4th, mandated the residents to evacuate to safe places. Later in the evening, the earthquake destroyed the populated city of one million residents. Most of the buildings had been built without the strength to withstand the earthquake. But the evacuated people were saved. The evacuation resulted in mitigating the loss of human lives (casualties of ~ 2000). At the time, this episode was considered a successful prediction, inspired optimism for earthquake prediction, and stimulated the start of large earthquake prediction research projects around the world. Many new networks of seismic instruments were installed to acquire high-quality data.

In the following year, 1976, an Mw7.6 earthquake struck the city of Tangshan, which had one million inhabitants, and is about 500 km southwest of Haicheng. There were some abnormal phenomena observed prior to the earthquake. But the authorities did not have a consensus for the impending large earthquake as no foreshocks preceded it, and did not issue an evacuation order for most of the areas. This earthquake caused over 250,000 fatalities and many injured victims, leaving the optimism s for prediction somewhat short-lived. Nonetheless, governmental funding for long-term large projects for prediction research continued in Japan, the USA, China, and other countries hoping to observe precursory seismic events or any phenomena associated with an impending earthquake. Scientists have tried to characterize earthquakes using abundant data accumulated by newly installed seismic network stations. However, there is no reliable track record of detecting precursors or associated deformation that can be used to identify an impending large earthquake such as the Mw9 Tohoku-Oki in 2011. The hypothesis in prediction research, that precursors should be detected before an earthquake, has not been verified even using a dense seismograph network. Now we know what earthquake phenomena are, and it is not easy to detect precursors for an impending destructive event from geophysical observations or characterize the entire process of future earthquake ruptures.

One of the major outcomes from the EPP research was the mapping of areas with high seismic risks based on past earthquakes. But it is a fact that most of the recent major earthquakes occurred in areas where high seismic potentials had not been assigned on the basis of probability theory, e.g., the Kobe earthquake in 1995, the Tohoku-Oki earthquake in 2011, and the Kumamoto earthquake (Mw7) in 2016 (see “ Recent destructive earthquakes vs. seismic hazard risk maps ” section). The first major study to predict probabilities of strong ground motion based on historical earthquakes was published by Kawasumi ( 1951 ). The probabilistic hazard map was used as a basis of the Building Standard Law in Japan. Figure  4 shows one example of seismic risk estimates in comparison with the actually measured seismic intensities: (a) comparison of predicted peak ground accelerations (PGAs) (gal) for the entire country for the coming 100 years published in late 1980s (Architectural Institute of Japan 1987 ) and observed PGAs (numbers in red) within 10 years of the publication; and (b) measured I JMA s for the 1995 Kobe earthquake. The PGA predicted for the area including Kobe was between 200 and 300 gal. The Mw6.8 Kobe earthquake struck a broad region in southwestern Japan, and measured PGAs of over 800 gal in and around the heavily damaged zones of Kobe City. This earthquake caused the worst catastrophe (6434 fatalities, and very large economic losses) in Japan since the 1923 Great Kanto earthquake (~ Mw8). High seismic potential had not been assigned for the area in EPP, either.

Comparison of predicted seismic risk and measured intensities: a map of peak ground acceleration (PGA) distribution (shown with contours of gal) estimated for 100 years over the country published by Architectural Institute of Japan ( 1987 ). The original map was derived based on the historical seismic activity by Kawasumi ( 1951 ), and was reflected in the modified Building Standard Law in 1981. Actually measured PGAs (in red) that exceeded 600 gal at JMA and NIED strong motion stations in the 1990s including the 1995 Kobe earthquake are indicated; b measured intensities for the 1995 Kobe earthquake. PGAs of over 800 gal were recorded by two instruments in Kobe and assigned intensities ( I JMA ) were 6, whereas the predicted PGA for this area was 200–300 gal as is shown in ( a ). Note that at the time, the I JMA scale was described with seven levels from 0 to 6, and was modified to 10 levels (0, 1, 2, 3, 4, 5−, 5+, 6−, 6+, 7) based on the instrumental data in 1996 (see Midorikawa et al. 1999 )

Following the Kobe earthquake, Japan has developed the world’s most extensive real-time earthquake monitoring network, installing many observation arrays of borehole seismic stations and dense strong-motion instruments (Okada et al. 2004 ) as well as extensive GPS arrays (e.g., Sagiya 2004 ). The dense real-time seismic network enabled the country to start the nationwide EEW system that has been operational in public under JMA since 2007 (Hoshiba et al. 2008 ). Independent of the nationwide EEW system, on-site real-time monitoring systems have been developed individually and dedicated for specific facilities in railway operations (e.g., Japan Railways) and industries in private sectors (semiconductor factories, chemical plants, etc.).

On-site real-time monitoring for front alarming

There have been ideas to forecast strong ground shaking at a site from an earthquake that occurs at a distance. The system would rely on signals from an earthquake detected at a nearby site, and transmit an alarm to affected areas at a further distance before strong ground shaking arrives. This kind of idea for front alarming already existed in the nineteenth century, and was presented by J. D. Cooper in San Francisco in 1868. However, the idea could not be implemented due to technical limitations at that time (Nakamura and Tucker 1988 ). Late in the twentieth century, dedicated on-site real-time monitoring systems were developed for specific facilities to detect an event and take necessary actions before strong shaking arrives. Research and development into how to forecast strong ground shaking at a given site has been carried out, independent of the big network installation and prediction research in Japan.

The most notable front alarm system was developed systematically by the Japanese National Railways (JNR 1949–1987, now Japan Railways Group) to secure safe railway operation, i.e., protect from earthquakes trains running at high speeds. Nakamura ( 1996 ) reviewed the R&D history of the real-time earthquake monitoring and front alarm system that led to the creation of the Urgent Earthquake Detection and Alarm System (UrEDAS). This system became fully operational in the 1980s, and was put to practical use for Shinkansen (bullet train) operation after an experimental period. Nakamura ( 1996 ) examined the railway damage on a magnitude (M) vs. epicentral distance (Δ) diagram (see Fig.  5 ; after Nakamura ( 1996 )) that shows railway damage may occur for earthquakes with M ≥ 5.5, in the areas of epicentral distance up to ~ 5 km for M~ 5.5, and over 100 km for M > 7.5. Some follow-up surveys of railway damages using more recent earthquakes also indicate a similar trend, a certain correlation between M and Δ (e.g., Ashiya et al. 2007 ). If the on-site monitoring system estimates the magnitude and epicentral location rapidly using the beginning part of seismic signals, it can issue an alarm to trains running in the affected areas to slow down and stop. The basic idea of this alarm system is to exploit the difference of P- and S-waves’ propagation speeds, i.e., ~ 6 to 7 km/s for P-wave and less than 4 km/s for S-wave near the ground surface. S-waves convey three to ten times stronger motions than P-waves, but propagate more slowly than P-waves.

Railway damage evaluated on a magnitude (M)-epicentral distance (Δ km) diagram. This is a copy of Fig. 2 in Nakamura ( 1996 ) with slight modification (courtesy by Y. Nakamura). The dashed horizontal line indicates the epicentral distance of 30 km. For M > 6.5, damage occurred at sites of Δ > 30 km. For ~ 5.5 < M  <  6.5, there was damage at some sites with Δ  <  30 km

UrEDAS calculates continuously at the instrument site parameters from real-time waveform data, such as back azimuths, the ratios of vertical to horizontal amplitudes to discriminate between P- and S-waves, and predominant frequency to estimate an earthquake’s magnitude. Once it detects an earthquake, preliminary earthquake parameters (i.e., magnitude, epicentral and hypocentral distance, etc.) are estimated quickly at the site. Then, it evaluates the parameters on the M – Δ diagram (similar to that in Fig. 5 ) and may issue an alarm if necessary. The process time was ~ 3 s in 1995. They continued improving the speed of accurate parameter determination and developed a new generation of UrEDAS, called compact UrEDAS, that could issue an alarm in 1 s (e.g., Nakamura 2004 ).

When the 2004 Niigata Chuetsu earthquake (Mw6.6, depth ~ 13 km) occurred, there were four trains running above the epicentral area. Four compact UrEDAS stations in the area detected the earthquake signals and issued an alarm in 1 s. Trains Toki 325 and Toki 332 received the alert 3.6 s after the earthquake origin time ( t o ), Toki 406 in 4.5 s, and Toki 361 in 11.2 s after t o . Immediately after they received the alert, these trains were slowed down automatically to stop. One of the trains (Toki 325), which slowed down but did not stopped completely, was derailed but without causing any injuries. This was the first occasion anywhere in the world that a dedicated real-time monitoring system prevented an earthquake disaster for running trains (Nakamura 2008 ).

Japanese industries in general reinforced factories and installed on-site real-time earthquake monitoring systems, especially after the 1995 Kobe earthquake. Takamatsu ( 2016 ) reported how semiconductor industries improved safety preparations against earthquakes. His company developed a combined use of alerts from the big-net EEW by JMA with their own on-site monitoring system; these supplement the deficiencies of each other to function effectively.

Big-net EEW systems

Eew system in japan.

After the 1995 Kobe earthquake, the National Research Institute for Earth Science and Disaster Resilience (NIED) led the installation of dense real-time seismograph networks in Japan. K-NET (Kyoshin network) is a network of strong-motion seismographs installed at more than 1000 observation stations on the ground surface that are distributed every 20 km uniformly over the country. It has been operated by NIED since June 1996. KiK-net (Kiban Kyoshin network) is a network of strong-motion seismographs deployed at approximately 700 stations nationwide, each of which consists of 2 sets of seismographs, 1 set installed in a borehole co-located with a set of Hi-net (high sensitivity seismograph network) instruments, and the other set on the ground surface. The strong-motion data recorded by K-NET and KiK-net are immediately transmitted to the data management center of NIED in Tsukuba and are made available to the public via a web site (Okada et al. 2004 ).

On the other hand, JMA and the Railway Technical Research Institute developed a system called Nowcast Earthquake Information System (Hoshiba et al. 2008 ) while NIED developed a real-time earthquake information system (REIS) (Horiuchi et al. 2005 ; Nakamura et al. 2009 ). Following these system and algorithm developments for real-time data analyses in October 2007, JMA started nationwide public operation of an EEW system by combining the products from REIS and Nowcast (Nakamura et al. 2009 ).

The Japanese EEW system that consists of over 4000 real-time monitoring stations (Fig. 3 a) can issue an alert to affected areas seconds before a strong hit if the areas are outside of the gray zone. The hypocenters and magnitudes are determined by a combination of several techniques using data from the JMA network, NIED’s Hi-net, and 30 KiK-net borehole stations in the Kanto region (Iwakiri et al. 2012 ). The system has been locating 10 to 20 events per day including a few felt earthquakes in and around Japan, and issues alerts when strong ground shaking ( I JMA  ≥ 5−) is expected. The system updates the earthquake parameters with more accurate information every second. This is a new application of real-time seismology for earthquake hazard mitigation. Since October 2013, NIED has been providing a real-time view of ground motions (PGA or PGV) recorded at K-NET and KiK-net stations over the country that are updated every second, overlaying the EEW monitoring screen (Kyoshin Monitor: http://realtime-earthquake-monitor.bosai.go.jp /).

In 2011, the extensive real-time monitoring and EEW system was operating across the country (e.g., Hoshiba and Iwakiri 2011 ; Hoshiba et al. 2011 ). The operations of the system along with individual on-site monitoring systems and engineering preparations have been tested effectively for advantages and shortcomings during the Mw9 earthquake. This earthquake on March 11th was preceded by an Mw7.3 foreshock on March 9th, and then ruptured the source areas of several previous large earthquakes (M > 7) in one sequence (Tajima and Kennett 2012 ; see the review by Tajima et al. 2013 ). The March 9th foreshock (Mw7.3) was first considered as “a typical large earthquake in the area” and a spokesman from JMA urged the general public to take precautions against its large aftershocks. It turned out that the March 9th event was a mere foreshock of the Mw9 main event that was the 4th largest event recorded instrumentally since 1900, and one of the seven deadliest events with more than 10,000 casualties in the twenty-first century.

It was a rare experiment of the nationwide EEW system to detect and estimate the magnitude of a large earthquake that has evolved to a great Mw9 while also issuing alerts with updated information, so the public can take response actions in a very limited time frame. The EEW system detected the event 22 s after the origin time and issued an estimated JMA magnitude (Mj) as 7.2 in 8.6 s after the event detection and continued updating the magnitude estimate up to Mj8.1 in 105 s. Mj8.1 was the final magnitude determined by the real-time system. Numerous research papers have been published, which study the Mw9 source characteristics that ruptured several previous source areas of an M7-class earthquake in one sequence of ~ 150 s duration taking a complex rupture process. The shortcomings of the EEW system identified in the parameter determination of the long-lasting complex source rupture process have been improved with inclusion of long-wavelength data (e.g., crustal deformation by GPS) to rapidly estimate the final size of a large earthquake in the real-time scheme (e.g., Ozaki 2011 ; Ohta et al. 2012 ; see reviews by Tajima et al. 2013 ). Lay ( 2017 ) provided a thorough review of published papers to date that detailed the complex source characteristics, revising the magnitude to Mw9.1. But only the gross picture of the rupture characteristics obtained from preliminary studies reviewed by Tajima et al. ( 2013 ) is necessary to review EEW application and practice.

As discussed in Fig. 1 , the warning time is critical. Figure  6 illustrates the warning times (shown on concentric circles from the epicenter) and the distribution of measured intensities for the Tohoku-Oki earthquake. At location A1, I JMA 6− shaking arrived in 6 s after the initial earthquake alert. Likewise, strong shaking of I JMA 7 arrived at A2 and I JMA 6+ at A3 in 19 s, I JMA 6+ at A4 in 35 s, and I JMA 6+ at A5 in 49 s, respectively. Location A6 is in Chiyoda-Ku/Tokyo where shaking of I JMA 5+ arrived in 67 s after the first alert issuance (see http://www.data.jma.go.jp/svd/eew/data/nc/rireki/201103.pdf#page=4 ).

Warning time and intensity distribution for the 2011 Tohoku-Oki earthquake. Here, the warning time is the time interval between the alert issuance and ground shaking arrivals estimated on concentric circles. This figure was copied from “Report of earthquake early warning alerts” (in Japanese) at: http://www.data.jma.go.jp/svd/eew/data/nc/rireki/201103.pdf#page=4 , and slightly modified with location labels of A1 to A6, and other labels into English. The scale of I JMA 4 to 7 is color coded below the map. At location A1 I JMA 6−, shaking arrived 6 s after the initial alert was issued. Likewise, strong shaking of I JMA 7 at A2 and I JMA 6+ at A3 in 19 s, I JMA 6+ at A4 in 35 s, and I JMA 6+ at A5 in 49 s, respectively. Location A6 is in Chiyoda-Ku/Tokyo where shaking of I JMA 5+ arrived in 67 s after the initial alert

Many on-site dedicated systems operated at individual facilities also detected the event and alarmed the facilities to take necessary actions including stopping trains, etc. JMA issued warnings for large tsunamis 3 min after the earthquake origin time (Ozaki 2011 ), which should have provided sufficient time for most people to escape. However, complications in the information transfer, and emergency response, partly due to the underestimated tsunami heights in the beginning, caused problems of miscommunication so that many people were killed by the huge onrush of water. Tsunamis completely destroyed many coastal cities and towns along the Pacific coast of northeast Japan, causing a large number of casualties and the much-publicized failure of a nuclear power plant in Fukushima. The estimated damage was over US$300 billion. Nonetheless, without the big-net EEW and on-site monitoring systems and intensive engineering preparations, the loss of lives and damage of facilities could have been far worse (see Tajima et al. 2013 ). The casualties of this earthquake (Mw9) were less than those caused by other smaller but destructive earthquakes in the Mw range of 6.6 to 7.9 (see Table 1 ). The relatively few casualties from the Mw9 earthquake are a testament to Japan’s emergency systems that include not only the big-net and on-site EEW systems but also the rigorous building codes and advanced engineering technologies for quake resistance (National Institute for Land and Infrastructure Management, and Building Research Institute 2012 ).

EEW system developments in other countries

Other countries including Mexico, the USA, Turkey, Taiwan, Korea, China, and countries in Europe have been also installing real-time network stations and developing EEW systems, for which there is an excellent review of progresses focusing on big-net EEWs around the world (Allen et al. 2009 ). Among the networks, the EEW system developed in Mexico by the Mexican Seismic Alert System (Spanish: Sistema de Alerta Sísmica Mexicano or SASMEX) is the first public EEW system in the world. This system was installed after the catastrophic 1985 earthquake (Mw8) and has been operational since 1993 (Espinosa-Aranda et al. 1995 ). At present, the network has about 100 seismic sensors with 28 more stations in development.

During the preparation of this article, there were two large earthquakes in Mexico, one (Mw8.2) occurred in the Gulf of Tehuantepec off the coast near Chiapas on September 7 and the other one with Mw7.1 in central Mexico on September 19 in 2017. The September 19 earthquake struck the greater Mexico City area. More than 200 buildings collapsed, 370 were killed, and ~ 6000 people injured. This earthquake coincided with the 32nd anniversary of the 1985 Mexico City earthquake (Mw8) that killed many thousands of people (in the range from ~ 5000 to 40,000), injured ~ 30,000 people, and destroyed many buildings including 412 that collapsed. When the September 19, 2017 earthquake occurred on land, ~ 120 km from Mexico City, people in the city felt ground shaking from P-waves a few seconds before they heard the alarm siren. Many people began evacuating buildings before the alarm sounded, according to surveillance camera records. The S-wave arrived in many locations in Mexico City about 6 s after the beginning of alarm siren and the shaking increased in amplitudes reaching a maximum after 17 s. Some buildings in the city collapsed completely within seconds of the beginning of the alarm siren (Nakamura 2017 ).

This event provided important reminders about EEW systems. EEW cannot prevent buildings from collapsing; but damage can be reduced or avoided if better building choices are made. If the seismic monitoring network coverage in the epicentral area were denser and the data processing were faster, there could have been a longer warning time in Mexico City (i.e., more than 20 s), instead of 6 s. If the buildings that collapsed had been reinforced to be earthquake resistant, they might have performed better during the September 19, 2017 quake. A high-standard building code and reinforcement for earthquake resilience must accompany an EEW system environment, as is widely adopted in Japan. No modern multi-story buildings have collapsed immediately during the shaking of any of the recent strong earthquakes in Japan.

In the western United States, the background seismicity is moderate. But the region does have high hazard, with destructive earthquakes (Mw ≥ ~ 6.7) likely along active faults. The 1989 Loma Prieta (Mw6.9) and the 1994 Northridge (Mw6.7) caused serious damage in urban areas in northern and southern California, respectively. The Loma Prieta event ruptured a small portion of the zone along the southern section of the fault previously ruptured by the 1906 San Francisco earthquake (Mw7.9, with the estimated maximum Modified Mercalli Intensity (MMI) of IX ( Extreme ) in San Francisco and several other cities, casualties of ~ 3000, over 80% of the City of San Francisco was destroyed). Thus, a large event (Mw > 6.7) is still likely to occur in the region. The Northridge earthquake occurred on a blind thrust fault near the epicenter of the 1971 San Fernando earthquake (Mw6.6, maximum MMI of IX) and directly hit Los Angeles, a populated US city, for the first time since the 1933 Long Beach earthquake (Mw6.4).

At the urging of scientific groups and organizations, the Federal and state governments funded the installation of an improved real-time monitoring network and the development of an EEW system, ShakeAlert. It now covers a broad range along the West Coast of the United States (Given et al. 2014 ; Strauss and Allen 2016 ; Cochran et al. 2017 ), but the station distribution is not homogeneous as shown in Fig. 3b . In the populated areas with high seismic risk, i.e., the greater Los Angeles, San Francisco Bay, and Seattle areas, the station spacing is about 10 km. The installation goal for other areas with low population but high hazard risk is 20 km between stations. Regions where there is no known seismic hazard and low population, the station spacing goal is 40 km (Peggy Hellweg, personal communication).

An EEW system in a big-net environment promises results similar to those in Japan, western United States, and other regions/countries, i.e., alerts will be delivered seconds before strong shaking starts. To achieve the goal, they target achieving similar station coverage to Japan’s modern system of networks, as well as data analysis speed and accuracy. It is important that recipients/end-users of EEW alerts in other countries realize that seismic safety preparations at individual (recipients) sites are very advanced in Japan, likely more advanced than in many other countries. This is in addition to its earthquake-resistant building code. In Japan, private sector industries build resilient structures that damp or are isolated from strong ground shaking, in addition to using on-site seismic monitoring and calibration using data from background seismic activity. Excessive emphasis on station coverage density and rapid real-time data analysis may distract from the key issues of seismic safety preparations which must be the foundation on which EEW relies.

The review by Allen et al. ( 2009 ) lists concerns about algorithms for detecting seismic signals, uncertainties in the conventional ground motion prediction equations (GMPE), speed of analysis and accuracy in location, and magnitude determinations. In fact, estimates of ground motion intensities by a big-net EEW system as a function of distance using GMPE are not always in agreement with observed intensities. Actual intensity distributions in felt earthquakes show short-wavelength variation. The frequency band in which seismic intensities are determined is from ~ 0.5 to 10 Hz, and thus there are significant effects from the response of local subsurface structure. Here, note that the incident azimuth of the incoming wave (propagation direction) and a site amplification factor also affect the ground motion on the surface at a site, resulting in its deviation from estimates based on a conventional GMPE (Hayashida and Tajima 2007 ). The variability can be substantial depending on earthquake locations (hypocenters). Because of the spatial resolution associated with station spacing (~ 20 km) and the required speed for alarming, it may not be practical or meaningful for big-net EEWs to model all possible variability.

Figure 2 illustrates an example of hypothetical scenarios during an earthquake at a high-tech facility. The facility contains expensive machinery that runs under high voltage and high currents to produce a strong magnetic field, and is vulnerable to strong shaking when it is in operation. In the event of a strong quake, the facility may receive an EEW alert. Automatically, the power will be shut down. The building will not collapse assuming it was built with a high-standard building code. But the expensive machinery may not stop operating instantaneously. If the machine in operation is hit by a strong quake directly, it will be damaged severely. It must be protected with a system to isolate or damp strong ground shaking beneath the building. In any case, an isolation (or damping) system should work effectively, regardless of an earthquake location since the warning time may be very short before shaking from a strong quake begins. Without such a system, the economic loss in terms of expensive machinery will be large. In reality, it is not a common practice to install (include) a damping or isolation system beneath the facility in southern California, where background seismicity is moderate. It is considered to be expensive. This situation evokes concerns not only about EEW alerts provided by a big network and on-site monitoring systems but also collaborations with seismic engineers during the construction phase. An incremental improvement in the speed of determining accurate earthquake parameters will not make a difference in the actions needed to effectively mitigate the hazard at the site.

Recent destructive earthquakes vs. seismic hazard risk maps

As stated in subsection “ From Earthquake prediction to real-time monitoring ,” one of the major outcomes from the EPP research in Japan was to map the areas with high seismic risks based on the past large earthquakes. But it is a fact that most of the recent destructive earthquakes occurred in the areas where the assigned seismic potential was not particularly high relative to other areas, since the history of instrumental seismic observation was not long (~ 100 years at the time), and the information from ancient documents which described earthquake damage was found mostly in populated cities, and not homogeneous. Besides, the published hazard risk maps were prepared for experts using probabilistic methods for risk estimates and are not easy to understand what these values mean. Figure  7 is an example of probabilistic seismic hazard map for ground motion for the entire country that shows the probabilities predicted for ground motion of I JMA  ≥ 6− and I JMA  ≥ 6+ for the next 30 years (published in 2010). The probabilities for I JMA  ≥ 6− assigned for the regions of strong ground shaking by the 2011 Tohoku-Oki and 2016 Kumamoto earthquakes were low, mostly under 6%, although it is not clear what the difference between the probability values actually means from a practical viewpoint.

Probabilistic hazard map of ground motion for 30 years published in 2010. The original figures were downloaded from the website of the Headquarters for Earthquake Research Promotion: ( https://www.jishin.go.jp/evaluation/seismic_hazard_map/shm_report/shm_report_2010/ . https://www.jishin.go.jp/main/chousa/10_yosokuchizu/ka_bunpu.pdf ). This site is in Japanese only; a corresponding site in English is https://www.jishin.go.jp/main/index-e.html that shows only the 2007 version and is not updated. There are areas along the Pacific coasts from Kanto to Shikoku and northeastern Hokkaido where ground shaking of I JMA 6− or greater was predicted to occur in the coming 30 years with a probability of more than 6% (left). Probabilities of 6% or higher for ground shaking of I JMA 6+ or greater were predicted only for narrow zones along the Itoigawa-Shizuoka Tectonic Line and the coast in Tonankai region, and for other small areas (right). Note that these hazard maps show low probabilities of less than 6% to most of the regions which were struck by strong ground shaking of I JMA 6− and greater during the 2011 Tohoku-Oki and the 2016 Kumamoto earthquakes

Figure  8 shows the measured I JMA ’s for the Mw9 Tohoku-Oki earthquake in 2011. The Tohoku region and part of the Kanto along the Pacific coast broadly suffered strong ground shaking of I JMA  ≥ 6− during the Mw9 earthquake. Figure  9 shows the I JMA distribution measured for the April 16, 2016 Kumamoto earthquake (Mw7.0). The instrument stations that measured intensities 6− and larger surround the epicenter (denoted with +) (top). The area of strong shaking around the epicenter is enlarged (lower left), and the estimated intensity distribution is shown by contours (lower right). The recorded I JMA ’s for the 2011 Tohoku-Oki and 2016 Kumamoto earthquakes far exceeded the predicted I JMA ’s in 2010.

Observed intensity distribution for the 2011 Tohoku-Oki (Mw9). Intensities from 3 to 7 ( I JMA ) measured by instruments are shown at seismic stations (left) and the estimated distribution of intensities by contours (right) (the figures were downloaded from: http://www.data.jma.go.jp/svd/eqev/data/2011_03_11_tohoku/index.html ). The intensity scale is color coded from 1 (white) to 7 (pink), and the red star shows the epicenter. The region with I JMA 6− and greater has a stretch of over a few hundred kilometers along the Pacific coast

Intensity ( I JMA ) distribution measured for the April 16, 2016 Kumamoto earthquake (Mw7.0). The instrument stations that measured intensities 6− and larger are located in the area surrounding the epicenter (+) (top). This area around the epicenter is enlarged (lower left), and the estimated intensity distribution is shown by contours (lower right)

Feedback from big-net beta-phase tests

We participated in the beta-phase test of the nationwide EEW system in Hiroshima, western Japan from 2003 to 2007. During the period, we experienced a few felt earthquakes, but not any destructive events in the area. The events were located more than 100 km away since the local seismic activity in that region is not so high as in other parts of Japan. We received an alert more than 20 s before we felt ground shaking, i.e., the warning time (the time interval between an alert and ground shaking arrival) was more than 20 s. We watched the expanding concentric circles for P- and S-wave propagation fronts from the epicenter on the monitor screen and confirmed that the system forecast the arrival time of S-waves well coinciding with weak felt motion. On the other hand, we learned that the actual ground motion intensities deviated from the intensities estimated using GMPEs on the smooth concentric circles. The complex subsurface structure should affect the ground motions on the surface, which are characterized by short-wavelength variation. The observed intensity variability depends on the incident azimuths and amplification factors from the borehole to the surface, and can be substantial (Hayashida and Tajima 2007 ).

In western United States, the beta-phase test of ShakeAlert started in 2013 (Given et al. 2014 ). During the period (as of September 2017), two moderate earthquakes (Mw 5.1 and 6) occurred in the network coverage. The Mw5.1 La Habra earthquake on March 28, 2014 caused moderate property damage around the epicentral area locally but no injuries. Recipients at a site of ~ 32 km from the epicenter received an EEW alert, and then in a couple of seconds felt weak motion. The warning time was a couple of seconds. The other earthquake of Mw6 occurred in South Napa near the San Francisco Bay area on August 24, 2014. The ShakeAlert team has been collaborating with the Bay Area Rapid Transit (BART), a local railway network, to help develop a system for safe train operation in the occurrence of earthquakes (Strauss and Allen 2016 ; Hellweg et al. 2016 ). They report that when the South Napa earthquake occurred, the earthquake alert was delivered successfully to the central system of BART operation 5 s after the earthquake origin time and 8 s before S-wave arrival to the center.

Here note that at the earthquake origin time (3:20 am), no trains were in operation, and the report of the EEW test with BART response is somewhat misleading for general public in terms of effective alert delivery. If trains were in operation near the epicenter, the time frame of 5 s for issuing an alert may not be sufficient for the centralized system to take action for train safety. In 5 s after the earthquake occurrence, the P-wave front propagates about 30 km and S-wave about 18 km from the epicenter. The big-net EEW system may not issue an alert before strong shaking to affected areas in the gray zone for this earthquake while an Mw6-class earthquake can cause railway damage in areas up to ~ 30 km from the epicenter (refer to Fig. 5 ). The BART central system is located ~ 45 km away from the epicenter since the S-wave arrival was 13 s after the origin time, and thus received the alert 8 s before S-wave arrival (see the timeline in Fig. 1 ).

In the past half-century, three Mw6-class earthquakes caused serious damage in urban areas in California including casualties. The 1971 San Fernando (Mw6.6) recorded a maximum MMI of IX (violent) and caused casualties of 65, ~ 2000 injured, and over $500 million economy losses. The 1989 Loma Prieta (Mw6.9) also recorded a maximum MMI of IX, and caused 63 fatalities, ~ 4000 injured, and ~ $6 billion losses. The 1994 Northridge earthquake (Mw6.7) occurred at a hidden fault in close proximity to the 1971 San Fernando earthquake along the southern edge of the western Transverse Range in southern California. The San Fernando aftershocks form a plane extending from a depth of 15 km to the surface, dipping toward the northeast at about 40°. The Northridge aftershocks delineate a fault extending from a depth of 18 km to about 5 km, dipping toward the southwest at about 40°. The fault planes of the two comparable earthquakes dip in opposite directions and represent conjugate faulting in a compressive stress regime (Hauksson et al. 1995 ; Mori et al. 1995 ). The Northridge earthquake measured the peak acceleration of 1.8  g (16.7 m/s 2 ) at a strong-motion station about 7 km south of the epicenter. It was the highest ground motion ever recorded instrumentally in an urban area in North America, causing damage in the local area around Los Angeles (with the death toll of 57, more than 8700 injured, and the estimated property damage of ~ $30 billion or more). The damage caused by these earthquakes concentrated in the areas around the epicenters (within the gray zone, e.g., with an epicentral distance of less than 30 km).

In the beta-phase tests of big-net EEW systems, both in Japan and the western United States, we have not experienced any destructive earthquakes. But we learned important lessons regarding the limited time frame of alert delivery from a big-net EEW system and necessities for site-effective safety preparations. In the 2016 JpGU-AGU joint session on EEW, there were reports of big-net teams, i.e., from JMA in Japan, CISN in USA, Central Weather Bureau of Taiwan (CWBT), and Centro de Instrumentación y Registro Sísmico A.C. (CIRES) in Mexico (see http://www2.jpgu.org/meeting/2016/PDF2016/S-SS01_all_e.pdf ). They focused on the improvements of computation algorithms for speeding-up accurate parameter determinations in real-time, or planning to increase the density of network station coverage. Big-net teams also suggest that end-users (recipients) develop systems to make use of EEW alerts, which are to be delivered from a big-net system.

Here, we would like to reiterate that end-users can receive an alert from a big-net EEW system before a strong hit if their locations are outside the gray zone. If they are closer to the epicenter, they may not receive an alert before strong shaking while stronger ground shaking may hit the sites causing more serious damage (Fig. 1 ). Remember the successful use of UrEDAS system in the 2004 Niigata Chuetsu earthquake described in subsection “ On-site real-time monitoring for front alarming ,” i.e., the on-site system detected an earthquake (Mw6.6, depth ~ 13 km), issued an alert within a few seconds from the earthquake origin time to nearby trains which were running at high speeds, and slowed and stopped the trains. The system successfully prevented a disaster for the trains (Nakamura 2008 ).

The feedback from the beta-phase tests highlights key issues to be addressed for earthquake safety preparations using alerts from big-net EEW systems.

Variation of ground motions and site-specific factors

A big-net EEW system estimates seismic intensities in the affected areas using a conventional GMPE, once the earthquake parameters (hypocenter and magnitude) are determined. Here, a GMPE relies on probabilistic distributions of observed ground motion parameters (i.e., peak ground acceleration, peak ground velocity, seismic intensity) for a given earthquake magnitude, source-to-site distance, etc. The station spacing is roughly the measure of optimum resolution and calculation speed of a big-net system, and may be larger than 20 km if the network is during a development stage. Nonetheless, the ground motion variation in a critical frequency band of ~ 0.5 to 2 Hz is in a much smaller scale than a typical big-net station interval, i.e., ~ 10 to 20 km. In fact, observed intensities of ground motions are not necessarily in agreement with the estimates by a big-net EEW. Recently, researchers proposed a new method of “PLUM” (Propagation of Local Undamped Motion) for the JMA EEW system to better predict seismic intensities directly from the observed real-time intensities near target sites (Kodera et al. 2018 ).

We did some exploratory studies to visualize the variability of ground motions using data from a dense local strong motion network in Yokohama City (Yokohama City strong motion network, YCSMN). We also have some results from KiK-net data analyses to illustrate the local subsurface effects among the stations, and event location dependency for ground motion variability.

Short-wavelength variation of ground motions

There is a dense network of strong-motion instruments that has been operating within a small area (~ 20 × 30 km 2 ) in Yokohama City since 1999. The total number of stations was 150 at the peak with its average station interval of ~ 1.5 km or less, whereas it is reduced to 42 now. The data from this network show substantial variation of ground motions. Figure  10 compares the estimated JMA seismic intensities based on the JMA EEW scheme (left) with calculated ones using data from YCSMN (right) for two earthquakes. Note that the JMA EEW system computes I JMA using the GMPE with closest distances from the estimated earthquake fault but does not include information regarding site-specific conditions (see the method for estimating I JMA in the JMA EEW system in Appendix ). The estimated intensities in the figure are the results of ex-post evaluations. The site amplification coefficients were obtained from averaged values of S-wave velocity in the upper 30 m (AVS30) at each grid point (at 250 m interval) in and around Yokohama City, provided by the Japan Seismic Hazard Information Station (J-SHIS, by NIED).

Comparison between JMA seismic intensities estimated in the real-time EEW scheme of JMA (left) and calculated intensities using data from YCSMN stations (right). The procedure of intensity estimation is explained in Appendix . Here, we used the site amplification map of the Japan Seismic Hazard Information Station (J-SHIS by NIED) to obtain site amplification coefficient at each grid (250 m interval) for the JMA estimates. The JMA intensity scale ( I JMA ) is shown at nine levels from 1 (white) to 7 (purple): a EQ1: July 23, 2005 NW Chiba Pref. (Mw5.9, depth 73 km; see Table 3 in Appendix ). The I JMA s estimated in the EEW scheme are 4 (green) in the whole area, regardless of different site conditions. The actual I JMA s calculated with data from the YCSMN (142 stations) vary from 5− (yellow) on the northeast side to 3 (light blue) on the southwest and northwest sides. b EQ2: March 11, 2011 off the Pacific Coast of Tohoku (Tohoku-Oki; Mw9, depth 24 km). The I JMA s estimated for Mj8.1 using the real-time EEW algorithm in 105 s after P-wave detection were 4 (green) in the basin and 3 (light blue) on the hillside. The actual I JMA s calculated at the YCSN stations (118 stations) range from 4 (green) to 5+ (orange)

Figure  10a shows the estimated and observed seismic intensities for a local earthquake (Mw5.9, Mj6, depth 73 km) in NW Chiba Prefecture on July 23, 2005 (see the event parameters in Table  3 in Appendix ). The intensities estimated by the JMA processing are 4 in the entire area, regardless of different site conditions. The actual intensities calculated using data from the YCSMN stations are from 3 on the southwest and northwest sides, to 5− on the northeast side showing short-wavelength variation, as short as the station interval of ~ 1.5 km or less. Figure  10 b compares the estimated and measured intensities for the 2011 Tohoku-Oki earthquake (Mw9, depth 24 km). In the beginning, i.e., in 8.6 s after the P-wave detection preliminarily determined Mj was 7.2, and the estimated intensities were 3 on the hillside and 4 in the deep sedimentary basin. Mj was updated repeatedly up to 8.1 (final value in EEW) in 105 s after the P-wave detection. Even for Mj8.1, the estimated intensities were roughly 4 all over the city. However, the actual intensities calculated using data at YCSMN stations range from 4 on the hillside to 5+ at some stations in the basin area. Here, note that the JMA EEW system computed seismic intensities using the GMPE of Si and Midorikawa ( 1999 ) with “the simplified closest distances” from the estimated earthquake fault, which is assumed a circular fault (see “Estimation of seismic intensity in the JMA EEW system” in Appendix ). In the case of a large earthquake, the source duration becomes long and the rupture process becomes complex, making it difficult to estimate the final magnitude and fault size in a real-time processing. For the 2011 Tohoku-Oki earthquake, more than four source areas on the fault contributed to strong-motion generation (e.g., Kurahashi and Irikura 2011 ; Asano and Iwata 2012 ). The JMA system repeatedly updated source information (hypocenter and depth), and we used the final estimation of hypocenter location (15.9 s after the detection) and magnitude (116.8 s after the detection), to derive the distance. The intensity estimation based only on the GMPE is not necessarily adequate for such a complex long-lasting rupture characteristic.

Using other local event data, we tested the effects of incident azimuths and angles of incoming waves on the ground motion variation that also depend on the propagation distance. Figure  11 shows an example. We used three shallow earthquakes of a similar magnitude (Mw ~ 4.8 to 5.2), which are located at distances of 60~70 km from the network (see the event parameters in Table 3 in Appendix ). The map shows the event locations relative to YCSMN. The incident azimuths of seismic waves are roughly from (a) south; (b) east; (c) west. For these events, there is no noticeable difference among the intensities estimated with the JMA algorithm since the magnitudes and epicentral distances are similar to each other. However, the observed ground motions at YCSMN stations show short-wavelength variation in the intensity range from 2 to 4 and different patterns among the events, reflecting 3D structural effects along the propagation paths. Our examination of YCSMN data suggests the scale of ground motion variation and the limitation of spatial resolution provided by a big network.

Variation of I JMA s calculated at YCSMN stations for three shallow local events (hypocentral distance R ~ 60 to 70 km) with a moment magnitude (Mw) from 4.8 to 5.2 (see the list of earthquakes in Table 3 in Appendix ). The incident azimuths of seismic waves are roughly from a south for EQ3; b east for EQ4; c west for EQ5. Note the variation of intensities observed at stations in YCSMN among the events even though the epicentral distances and the magnitudes are similar to each other

KiK-net data analysis

More than a decade ago, we investigated the subsurface amplification using data recorded in the borehole and on the ground surface at KiK-net stations in Hiroshima Prefecture (Hayashida and Tajima 2007 ). Here, we selected Hiroshima Prefecture as the study area since the depth to the bedrock (where V s > 2500 m/s) is shallow (less than several hundred meters) and the KiK-net borehole sensors are located on hard rocks. Thus, it is suitable to test the local amplification of seismic waves from the bedrock to ground surface using data recorded at relatively stable stations in Hiroshima. In this section, we follow up the study by Hayashida and Tajima ( 2007 ) using some recent data.

Figure  12 shows examples of integrated accelerograms (i.e., velocity waveforms) recorded at three KiK-net stations for an earthquake of November 21, 2011 (Mw5.2, depth 12 km; Eq. 6 in Table 3 in Appendix ) in Hiroshima Prefecture. The borehole sensors of these stations are located at ~ 200 m depths and S-wave velocities around the sensors are greater than 2500 m/s (see Table  2 ). The epicentral distances range from 45 to 69 km. There is no large difference among the peak ground velocities (PGVs) recorded at the borehole stations, but the PGVs on the surface are substantially different from each other. Especially, at station HRSH03, the ratio of the PGV on the ground surface to that in the borehole is greater than 14, while the ratios are less than 2 at station HRSH04. A large amplification at HRSH03 was observed also for other events (e.g., the 2001 Mw6.8 Geiyo earthquake; see Hayashida and Tajima 2007 ).

Three-component velocity seismograms recorded at three KiK-net stations (shown with solid triangles with HRSH01, HRSH03, HRSH04) in the borehole (upper three traces) and on the ground surface (lower three traces) in Hiroshima Prefecture for an event of November 21, 2011 (Mw5.2) (EQ6 in Table 3 in Appendix ). Here, the waveform amplitudes are normalized by the maximum amplitude at each station, and the number above each trace is the PGV (cm/s)

Figure  13 compares the observed and estimated PGVs at the three stations for earthquakes that occurred from August 1998 to August 2017 (3.4 ≤ Mj ≤ 7.1; at epicentral distances between 24 and 200 km). The diagonal lines show one-to-one correspondence of the estimated and observed amplitudes. Here, the PGVs are estimated following the procedure in the JMA EEW scheme (see Appendix ). The site amplification factors were adopted for AVS30 values from PS logging data (red square) and AVS30 map of J-SHIS (blue square) at each site (see Appendix for “AVS30”). The AVS30 values obtained from PS logging are smaller than those by J-SHIS, but the differences are not large at stations HRSH01 and HRSH03 (Table 2 ). At station HRSH04, there is a discrepancy in AVS30 values between PS logging and J-SHIS: the PS logging AVS30 is larger and the J-SHIS AVS30 substantially smaller than those at the other two stations. At HRSH04, the estimated PGVs derived with J-SHIS AVS30 data are larger than the observed amplitudes, and those derived with PS logging data show better fit to observed amplitudes. In general, the diagrams show that the PGVs are underestimated at station HRSH03 for most of the cases while the estimated values tend to be larger than the observations at HRSH04, especially when the site amplification coefficient is adopted from the J-SHIS map. The results from this study indicate the difficulty of accurate ground-motion estimation even at a site where the soil information is well investigated.

Comparisons between observed (vertical axes) and estimated (horizontal axes) peak ground velocities (PGVs) at three KiK-net stations (HRSH01, HRSH03, HRSH04) in Hiroshima Prefecture for earthquakes in the magnitude range from 3.4 to 7.1, from 1998 to 2017. The estimated PGVs are derived using the GMPE by Si and Midorikawa ( 1999 ) that is used in the EEW scheme of JMA. Here, the site amplification coefficients are derived using AVS30 values based on PS logging at each Kik-net station (red squares) and nationwide site amplification map, J-SHIS (blue squares), respectively (see the AVS30 values in Table 2 ). The diagonal indicates a one-to-one proportional line

In general, ground motion at a certain site fluctuates from the estimated value based on a GMPE due to specific effects between a source and the site that depend on rupture property on the fault, three-dimensional heterogeneity in the propagation path, and soil structure beneath the site. The diagrams in Fig.  13 do not distinguish effects among propagation distances, incident azimuths of incoming waves, and magnitudes. Possible combinations of an incident azimuth and an incident angle are almost infinite so that it is not practical to derive a formula that accounts for all these effects at each station in a big-net EEW system. There are some attempts to correct site amplification factors in real-time shake mapping (Hoshiba 2013 ; Hoshiba and Aoki 2015 ) or to derive a new GMPE to apply up to M9 mega-earthquakes (Morikawa and Fujiwara 2013 ). In spite of some incremental improvements in shaking estimates on the surface, we should accept the fact that the actual intensity of ground motion at a site may differ from the intensity estimated by a big-net EEW system due to the restriction of spatial resolution and rapid processing demand. After all, what end users/recipients of EEW alerts can do or should do for earthquake hazard mitigation may not change even after an incremental improvement of station coverage and the speed of accurate parameter determination in a big-net system. They should arrange their environment (residence, work place, etc.) for earthquake safety and resilience in advance.

Conclusions

Earthquake prediction has long been explored in the effort to mitigate the hazards from earthquakes. The drive to observe precursory events or deformation associated with impending large earthquakes contributed to the improvement of seismic instrumentation and the installation of many seismic networks around the world. On the other hand, ideas for a front alarming system existed already in the nineteenth century, but could not be implemented due to technical limitations. These limitations led to development of dedicated on-site real-time monitoring systems that could be installed in specific facilities late in the twentieth century. The on-site systems detect an earthquake and automatically initiated necessary actions before strong shaking arrives. Public real-time monitoring networks have evolved into EEW systems based on a concept similar to a front alarming system. Now big-net EEW systems are operational in several countries around the world.

The system of SASMEX in Mexico is the first public EEW system in the world and has been operational since 1993. Recently, deficiencies of the system itself and the lack of “site-effective preparations” were exposed in the September 19, 2017 Central Mexico earthquake (Mw7.1). The world’s most extensive nationwide EEW system has been operating in Japan since 2007, with a real-time monitoring network of over 4000 seismic stations. The advantages and shortcomings of the system, in conjunction with individual on-site monitoring systems and engineering preparations, were tested effectively during the rare Mw9 Tohoku-Oki earthquake in 2011. Many other countries attributed the lower number of fatalities from the Tohoku-Oki earthquake largely to the nationwide extensive EEW system, and now are in the process of installing or planning big-net real-time monitoring and EEW systems similar to that of Japan.

We participated in the big-net EEW beta-phase tests in Japan and the western United States. Our experience led us to have concerns about the general public’s expectations of EEW, mainly as they are not informed of what may be the limitations of the system under a certain conditions. They assume that they will receive an EEW alert before strong shaking arrives, regardless of their location relative to the epicenter, and that they will be able to take necessary actions to protect themselves such as “drop, cover, and hold on” or protect their facility.

Here, a big-net EEW system issues an alert for strong ground shaking before its arrival to areas, which are outside of the gray zone specific for the system (e.g., typically within distances of ~ 20 to 30 km from the epicenter). The system is useful for a large earthquake because strong shaking propagates through a broad region. With timely and accurate warnings before strong shaking, EEW systems can contribute to saving the lives of many people and mitigate economy losses. But if recipients were within the gray zone, they would not receive an alert before strong shaking. Ground shaking is likely to be stronger within than outside the gray zone. A moderate earthquake of Mw6-class can also cause serious damage in the gray zone. Even if they are outside of the gray zone, the warning time may be very short, with only seconds before the strong shaking starts. If a building structure does not have the strength to resist damage from strong shaking, it may collapse, killing or injuring the people inside. At a high-tech facility, even though a building is unlikely to collapse, equipment that is vulnerable to strong shaking may be severely damaged if it does not include a seismic isolation system. An isolation (or damping) system is expected to be effective, regardless of an earthquake’s location as it responds to shaking, independent of whether an alert is timely or not. Without such systems, industries are likely to experience large economic losses due to damage to expensive machinery.

In regions where seismic risk is high, but on-going seismic activity is moderate, site-effective preparation for industrial facilities and machinery vulnerable to ground shaking is not a common practice. It is considered to be expensive. This situation suggests not only about EEWs provided by a big network and on-site monitoring systems but also collaborations with seismic engineers during the construction phase. An incremental improvement in the speed of accurate parameters determinations will not make a difference in the site-effective preparation for the mitigation of seismic hazards.

It has been more than a decade since the public operation started in Japan, while in western United States, it is still undergoing testing and is not yet available to the public. At the present stage, it should be a goal of the s cientists and engineers involved in EEW projects to inform the public about how big-net EEW systems work and to recommend that it be supplemented with site-effective preparations to maximize its benefits. These are critical issues to be addressed along with improvements in EEW networks and continuing developments of algorithms.

There is substantial variation of ground motions within typical station spacing of a large-scale monitoring, ~ 20 km. Simply increasing the density of station coverage in the real-time EEW system does not ensure seismic safety. The preparations necessary to improve seismic safety at specific sites will not change even if there are incremental improvements to the EEW system in big-net operations through station coverage and the algorithms. At an industrial site, combining alerts from a big-net EEW system and an on-site monitoring system may provide the most optimal ability to rapidly implement the automated responses that will most effectively reduce costly damage from earthquakes. Our conclusions are summarized as below:

The general public should be aware of the likely timeliness of EEW alert delivery in relationship to strong shaking. An alert is meaningful only if it is delivered sufficiently early to be able to take necessary actions before strong shaking. A big-net EEW system may provide a timely alert for a large quake (M > ~ 7) if the recipient’s site is located outside of a gray zone (e.g., ~ 20 to 30 km away from the epicenter). For example, ShakeAlert will provide timely alerts to end-users in western USA at a long-distance in the event of a great earthquake, like the 1700 Cascadia earthquake (M ~ 9). For sites located within a gray zone, end-users may not receive an alert before experiencing strong shaking. Smaller earthquakes (of a Mw6-class) can still cause serious damage in the epicentral area, which may be entirely within the gray zone, as was experienced in the recent earthquakes in California.

Site-effective preparations for seismic safety are important to maximize the benefits from a big-net EEW. The preparation needed is independent of incremental improvements of big-net station coverage and algorithms. EEWs are useful only if the strength and resilience of structures and operations are ensured before an earthquake happens. EEW is not a panacea—without preparations well in advance of an earthquake, the public expectation of safety through EEW will not be met and is similar to that to earthquake prediction in the past.

Ground motions from earthquake waves vary over distances much shorter than the typical big-net station spacing of ~ 20 km over a broad region. Variation of ground shaking should be evaluated for a shorter wavelength than a big-net station interval using a local-scale monitoring.

Assessment of the benefits and costs of advanced preparation for earthquakes can be critical in areas such as California/USA that have high risk of destructive earthquakes but only moderate seismic activity at present.

Abbreviations

American Geophysical Union

Bay Area Rapid Transit

Centro de Instrumentación y Registro Sísmico A.C.

California Integrated Seismic Network

Dense Ocean Floor Network System for Earthquakes and Tsunamis

• Earthquake early warning

Earthquake Prediction Plan

Ground motion prediction equations

Global positioning system

High sensitivity seismograph network

Japan Agency for Marine-Earth Science Technology

Japan Meteorological Agency

Japanese National Railways

Japan Geoscience Union

Japan Seismic Hazard Information Station

Kiban Kyoshin network

Kyoshin network

Modified Mercalli intensity

National Research Institute for Earth Science and Disaster Resilience

Peak ground acceleration

Peak ground velocity

Pacific Northwest Seismic Network

Research and development

Real-time earthquake information system

Urgent Earthquake Detection and Alarm System

Yokohama City strong motion network

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Acknowledgments

The 2016 JpGU-AGU joint session on EEW held at the 2016 JpGU Annual Meeting at Makuhari Messe in Chiba/Japan led us to write this article. We thank all the participants who helped organize the session, presented papers at the oral or poster sessions, stimulated the discussion, and exchanged information. We appreciate Editor Chen Ji, and two anonymous reviewers for the review comments, which greatly improved the original manuscript. We are indebted to Yutaka Nakamura at System & Data Research Co. for providing us with the information, opinions, a scanned figure (railway damage on a diagram of magnitude vs. epicentral distance used for Fig. 5 ), and criticisms. We thank Peggy Hellweg at UC Bekeley for providing us with information of the ShakeAlert system network stations. She also reviewed the manuscript to verify the description of EEW developments and provided us with helpful comments. We thank Mitsuyuki Hoshiba for providing us with updated information of the JMA network including the PLUM method. We used strong-motion data from YCSMN provided by City of Yokohama, and KiK-net data by NIED. We thank Ahyi Kim at Yokohama City University for her help to access data from YCSMN. We used Generic Mapping Tools (GMT) by Wessel and Smith ( 1998 ) to draw some figures.

Not applicable.

Availability of data and materials

The KiK-net waveform data were obtained from the download site http://www.kyoshin.bosai.go.jp/kyoshin/docs/overview_kyoshin_index_en.html of NIED. Those who would like to use KiK-net data should register at https://hinetwww11.bosai.go.jp/nied/registration/ and get user ID and password. The strong-motion data of YCSMN were obtained from the download site of City of Yokohama at http://www.city.yokohama.lg.jp/somu/org/kikikanri/jisin-data/ (note this site is only in Japanese). Before accessing data, a prospective user should submit an application at http://www.city.yokohama.lg.jp/somu/org/kikikanri/jisin-data/sinsei/ . The station distribution data for Fig.  2 were obtained from the JMA website for the Japanese stations ( http://www.jma.go.jp/jma/en/Activities/earthquake.html ) and UC Bekeley for ShakeAlert in July 2017.

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Fumiko Tajima

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FT initiated and directed the review project, and TH analyzed the recent/updated data to verify the key issues addressed in the article. FT organized and wrote the manuscript while TH provided additional contributions to it. Both worked together to finalize the manuscript. Both authors read and approved the final manuscript.

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Estimation of seismic intensity in the JMA EEW system

Here, the procedure of estimating the JMA intensity (scale 0, 1, 2, 3, 4, 5−, 5+, 6−, 6+ 7) in the EEW system is briefly explained. When seismic P -waves from an earthquake are detected using real-time waveform data, the hypocentral location (latitude, longitude, and depth) and JMA magnitude ( M j ) are determined. Then, the JMA EEW system estimates seismic intensities at any arbitrary site as follows:

At first, the estimated M j value is converted to M W using a simple empirical relationship (Utsu 1982 )

This value of M W is used to estimate the earthquake fault length L (km: Utsu 1977 )

Here, note that there was no clear definition for magnitude M in Eq. 2, and the system assumes that it is equivalent to an Mw.

Then, the minimum distance x (km) between the fault and the site is estimated as

where R is the hypocentral distance between the hypocenter and the site. When the derived x is smaller than 3 km, the value is fixed to 3 km. The peak ground velocity on the reference bedrock ( PGV 600 : cm/s) at the site is estimated as a function of the hypocentral depth D (km) and x using the GMPE of Si and Midorikawa ( 1999 ),

Here, the “reference bedrock” is defined as a site where the S-wave velocity is 600 m/s.

The EEW system does not estimate seismic intensities for an estimated hypocentral depth of deeper than 150 km since Eq. 4 was designed to explain the attenuation characteristics of ground motions for shallow (< 50 km) earthquakes. The estimated PGV 600 is converted to the peak velocity on the engineering bedrock ( PGV 700 : cm/s) as follows:

Finally, the peak ground velocity ( PGV S : cm/s) on the ground surface and the JMA seismic intensity ( I JMA ) are estimated as follows:

where ARV is the amplitude ratio of PGV S relative to PGV 700 (amplification coefficient) and the value is determined using the average shear wave velocity in the upper 30 m ( AVS 30; Matsuoka and Midorikawa 1994 ).

In Japan, the nationwide maps of AVS30 and ARV are published by several national institutions (e.g., NIED, Geospatial Information Authority of Japan (GSI)). If an estimated intensity is equal to or greater than 5− somewhere in Japan, the EEW system issues a warning to affected areas and performs consecutive operation as long as the source information is updated.

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Tajima, F., Hayashida, T. Earthquake early warning: what does “seconds before a strong hit” mean?. Prog Earth Planet Sci 5 , 63 (2018). https://doi.org/10.1186/s40645-018-0221-6

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Annual Review of Earth and Planetary Sciences

Volume 47, 2019, review article, earthquake early warning: advances, scientific challenges, and societal needs.

• Richard M. Allen 1 , and Diego Melgar 2
• View Affiliations Hide Affiliations Affiliations: 1 Department of Earth and Planetary Science, University of California, Berkeley, California 94720-4760, USA; email: [email protected] 2 Department of Earth Sciences, University of Oregon, Eugene, Oregon 97403-1272, USA; email: [email protected]
• Vol. 47:361-388 (Volume publication date May 2019) https://doi.org/10.1146/annurev-earth-053018-060457
• First published as a Review in Advance on January 30, 2019
• Copyright © 2019 by Annual Reviews. All rights reserved
• ▪  Earthquake early warning (EEW) is the rapid detection and characterization of earthquakes and delivery of an alert so that protective actions can be taken.
• ▪  EEW systems now provide public alerts in Mexico, Japan, South Korea, and Taiwan and alerts to select user groups in India, Turkey, Romania, and the United States.
• ▪  EEW methodologies fall into three categories, point source, finite fault, and ground motion models, and we review the advantages of each of these approaches.
• ▪  The wealth of information about EEW uses and user needs must be employed to focus future developments and improvements in EEW systems.

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Early detection of earthquakes using iot and cloud infrastructure: a survey.

1. Introduction

• Several types of seismic waves radiate from an earthquake’s epicenter. Sensors are activated by P-waves, which are weaker but move more quickly. Thereafter, sensors send signals to cloud servers for processing.
• Algorithms in the cloud server instantly determine the location, magnitude, and severity of an earthquake. How big is it? Who will suffer from this?
• The technology sends out an alert before slower but more destructive S-waves and surface waves arrive.
• We clarify why the EEWS is advantageous for smart cities.
• We emphasize the growth of IoT usage, as well as the IoT system framework in general and its constituent parts.
• We have developed a thorough taxonomy of IoT devices that includes various topics such as the source of data, environment, measured parameters, and factors of validation.
• We present a standard design for the IoT that takes into account potential emergency management.
• We discuss the verification and validation concerns related to using IoT-based EEWS.

2. Seismic Waves and Seismic Signal Processing Techniques

• Primary waves, also referred to as P-waves, are longitudinal compressional waves that move through the earth in a straight line. These waves are known as “primary” waves because they arrive first at seismograph stations, traveling faster through the earth than other types of waves. P-waves are pressure waves that can travel through any material, including fluids, and move at a speed that is around 1.7 times faster than that of S-waves. In contrast to S-waves, which are transverse waves that move side-to-side, P-waves are compression waves that cause particles in the material they are traveling through to move back and forth in the direction of the wave’s propagation. They take the form of sound waves in the air and move at the same velocity as sound waves, which is around 330 m per second on average. The ability of P-waves to travel through any material allows them to be used to study the interior of the earth. By measuring the time taken for P-waves to travel through the earth from an earthquake’s epicenter to a seismograph station, scientists can calculate information about the earth’s internal structure. For example, the average speed of P-waves in granite is roughly 5000 m per second, while in water, it is around 1450 m per second. This information can be used to create a detailed model of the Earth’s interior.
• S-waves, also known as secondary shear waves, are transverse waves that cause the ground to shift in a direction perpendicular to their propagation during an earthquake. These waves arrive at seismograph stations after P-waves, which are faster. S-waves have a horizontal polarization and move in a horizontal direction, causing the ground to shift from side to side. However, S-waves can only travel through solids since liquids and gases do not support shear forces. They move through any solid medium at a speed that is approximately 60% slower than P-waves. The absence of S-waves in the outer core of the Earth is consistent with the presence of liquid. This is because S-waves cannot propagate through liquids, and their absence indicates that the outer core is predominantly liquid. However, P-waves can propagate through liquids, which is why they can travel through the entire Earth. The study of seismic waves and their behavior has provided scientists with valuable insights into the structure and composition of the Earth’s interior.

3. IoT-Cloud Systems

3.1. iot systems.

• Providing the node with an interface that can collect data from the environment.
• Providing a tool for acquiring and analyzing data in order to derive knowledge from it.
• Taking action and communicating choices and information to the appropriate hubs.

3.2. Cloud and Fog Systems

4. iot-cloud-based eews.

• Simulation testing involves creating a virtual environment that simulates real-world conditions, including seismic activity and sensor data [ 200 , 201 ]. Simulation testing allows researchers to test the performance of an EEWS system under different scenarios, such as different magnitudes and distances of earthquakes and different types of seismic waves [ 202 ]. This technique can also be used to evaluate the effectiveness of different algorithms and parameters used in the system [ 203 ].
• Field testing involves deploying an EEWS system in real-world conditions and collecting data on its performance and reliability [ 204 , 205 ]. Field testing can provide valuable insights into the system’s performance under actual operating conditions, which may differ from those in a simulated environment. Field testing can also help to identify potential issues with the system, such as sensor malfunction or communication failures [ 206 ]. This technique can be time-consuming and resource-intensive, but it provides valuable data on the system’s performance and reliability in real-world scenarios [ 207 ].
• Data-driven analysis involves analyzing large datasets generated by an EEWS system to identify patterns and trends, which can provide insights into its performance and reliability [ 208 ]. Data-driven analysis can be used to identify correlations between sensor data and earthquake characteristics, such as magnitude, duration, and intensity [ 209 ]. This technique can also be used to identify anomalies in sensor data, which may indicate issues with the system’s performance or reliability [ 210 ]. Data-driven analysis can provide valuable insights into the performance and reliability of an EEWS system over long periods of time [ 211 ].

5. Validation and Verification Aspects

5.1. different categories of v&v techniques, 5.2. adaptation of v&v techniques for eews, 5.3. cost and limitations of v&v techniques, 6. open challenges, conclusions and future directions.

• Sensor network reliability and accuracy: One of the primary challenges in implementing IoT and cloud-based EEWS is ensuring the reliability and accuracy of the sensor network. These systems rely on a network of sensors to detect and measure seismic activity, making it essential to ensure that the sensors are functioning correctly.
• Real-time data processing and decision-making: EEWS require fast and accurate data processing and decision-making capabilities to provide timely alerts to people and organizations in affected areas. This requires sophisticated algorithms and real-time data processing capabilities, which can be challenging to implement in IoT and cloud-based systems.
• Secure communication channels: The transmission of data between sensors, cloud facilities, and other components in an EEWS must be secure to prevent unauthorized access and tampering. Ensuring the security of communication channels is a significant challenge in designing and implementing these systems.
• Heterogeneity and scalability: IoT and cloud-based systems are inherently heterogeneous, with devices and services from different manufacturers and with different capabilities. Ensuring seamless integration and scalability of these systems is a significant challenge, particularly as the number of devices and sensors in the network increases.
• Cost-effectiveness and sustainability: Implementing an EEWS using IoT and cloud facilities can be costly, requiring significant investment in hardware, software, and personnel. Ensuring the cost-effectiveness and sustainability of these systems is a significant challenge, particularly in regions with limited resources.
• Usability and accessibility: EEWS must be usable and accessible to people and organizations in affected areas, including those with limited literacy or technical skills. Ensuring the usability and accessibility of these systems is a significant challenge, requiring careful consideration of user needs and preferences.
• Privacy and ethical concerns: The collection and processing of data in EEWS raise privacy and ethical concerns, particularly as these systems become more sophisticated and widespread. Ensuring that these systems comply with relevant regulations and ethical principles is a significant challenge.
• Interference from environmental factors: EEWS can be affected by environmental factors such as electromagnetic noise and weather conditions, which can interfere with the accuracy and reliability of the sensor network. Ensuring the robustness and resilience of these systems is a significant challenge, requiring careful consideration of environmental factors.
• Continuous monitoring and maintenance: IoT and cloud-based EEWS require continuous monitoring and maintenance to ensure system performance and reliability. Ensuring the continuous monitoring and maintenance of these systems is a significant challenge, requiring robust and scalable infrastructure and skilled personnel.
• Development of more efficient and accurate sensors: Research and development should focus on developing more efficient and accurate sensors that can accurately detect and measure seismic activity while also being cost-effective and scalable.
• Integration of artificial intelligence (AI) and ML: The integration of AI and ML can help to improve the accuracy and reliability of data analysis algorithms used in EEWS. This can lead to faster and more accurate decision-making, improving the effectiveness of these systems [ 264 , 265 ].
• Standardization of communication protocols: The standardization of communication protocols can help to ensure the interoperability and scalability of IoT and cloud-based EEWS. This can simplify the integration of different devices and services, reducing the complexity of these systems.
• Adoption of free, open-source software: The adoption of free, open-source software can help to reduce the cost and complexity of developing EEWS while also encouraging collaboration and innovation in this area.
• Engagement with local communities: Engagement with local communities can help to ensure that EEWS are developed in a form that meets the needs and preferences of people and organizations in affected areas. This can improve the usability and effectiveness of these systems in real-world scenarios.
• Development of new funding models: The development of new funding models, such as public–private partnerships, can help to ensure the sustainability and scalability of EEWS. This can provide the necessary resources and expertise to develop and maintain these systems over the long term.
• The “last kilometer” problem: This problem is the difficulty of assuring prompt and efficient warning, communication, and reaction systems to people and communities in the final seconds before the occurrence of powerful and devastating S-wave shaking during an earthquake. In particular, it requires addressing densely populated areas where the window for preparation and evacuation is constrained, where there is a gap between earthquake EEWS and the capacity to reach and notify individuals in the impacted area. In order to protect people’s safety and well-being in the final crucial seconds before the arrival of the destructive seismic waves, this topic focuses on the necessity for the effective broadcast of alerts and emergency instructions.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Abdalzaher, M.S.; Krichen, M.; Yiltas-Kaplan, D.; Ben Dhaou, I.; Adoni, W.Y.H. Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey. Sustainability 2023 , 15 , 11713. https://doi.org/10.3390/su151511713

Abdalzaher MS, Krichen M, Yiltas-Kaplan D, Ben Dhaou I, Adoni WYH. Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey. Sustainability . 2023; 15(15):11713. https://doi.org/10.3390/su151511713

Abdalzaher, Mohamed S., Moez Krichen, Derya Yiltas-Kaplan, Imed Ben Dhaou, and Wilfried Yves Hamilton Adoni. 2023. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey" Sustainability 15, no. 15: 11713. https://doi.org/10.3390/su151511713

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REVIEW article

Understanding the social aspects of earthquake early warning: a literature review.

• 1 Joint Centre for Disaster Research, Massey University, Wellington, New Zealand
• 2 Massey Geoinformatics Collaboratory, Massey University, Auckland, New Zealand
• 3 Toi Āria Design for Public Good, Massey University, Wellington, New Zealand
• 4 Te Toi Whakaruruhau o Aotearoa, Massey University, Palmerston North, New Zealand
• 5 Sysdoc Ltd., Wellington, New Zealand

Earthquake early warning (EEW) systems aim to warn end-users of incoming ground shaking from earthquakes that have ruptured further afield, potentially reducing risks to lives and properties. EEW is a socio-technical system involving technical and social processes. This paper contributes to advancing EEW research by conducting a literature review investigating the social science knowledge gap in EEW systems. The review of 70 manuscripts found that EEW systems could benefit society, and the benefits may go beyond its direct function for immediate earthquake response. The findings also show that there are social processes involved in designing, developing, and implementing people-centered EEW systems. Therefore, social science research should not just be concerned with the end-user response but also investigate various stakeholders' involvement throughout the development process of EEW systems. Additionally, EEW is a rapidly evolving field of study, and social science research must take a proactive role as EEW technological capacities improve further and becomes more accessible to the public. To improve EEW effectiveness, further research is needed, including (1) advancing our understanding of why people take protective action or not, and ways to encourage appropriate action when alerted; (2) enhancing public understanding, investigating best practices for communicating, educating, and engaging with the public about EEW and overall earthquake resilience; and (3) keeping up with technological advances and societal changes and investigating how these changes impact communities' interactions with EEW from various standpoints including legal perspectives.

Introduction

When an earthquake occurs, an earthquake early warning (EEW) system can warn end-users further afield of the incoming ground shaking. The several tens of seconds of warning (to potentially as much as 120 s) from such systems provide potential benefits such as reducing injuries and fatalities, protecting infrastructure, reducing disruptions to services, and improving overall earthquake preparedness and resilience.

The last decade has seen the rapid development of methodologies and technologies that have given us a deeper physical understanding of earthquakes and improved EEW processes to achieve better earthquake warnings ( Allen and Melgar, 2019 ). As a result, many locations worldwide already have operational EEW systems that broadcast warnings to the public before strong ground shaking arrives. Examples of governmental or official EEW services include Japan ( Kodera et al., 2020 ), Mexico ( Santos-Reyes, 2019 ), Taiwan ( Wu et al., 2017 ), South Korea ( Sheen et al., 2017 ), and the West Coast of the United States of America ( Chung et al., 2020 ). In other places, such as India, Turkey, and Romania, EEW systems do not yet issue alerts to the public but send warnings to ‘advanced users', such as governmental units or industrial users ( Wang et al., 2020 ). Italy's EEW system is active in the Campania Region but is not yet available to the broader public ( Velazquez et al., 2020 ). Many other locations in the world are also exploring, developing, and testing EEW systems, for example, various regions in China, Europe and South America ( Wang et al., 2020 ).

Furthermore, EEW development is no longer limited to geographical jurisdictions. The ubiquity of technology allows EEW to be implemented across borders. The earthquake network (EQN) initiative, one of the earliest smartphone-based EEW systems, has provided EEW services across 25 countries since 2013 ( Finazzi, 2020 ; Fallou et al., 2022 ). Commercial counterparts can also provide EEW products and services. For example, a Google initiative introduced the Android Earthquake Alerts System in New Zealand and Greece in April 2021 ( Voosen, 2021 ) without the involvement of warning authorities from those countries ( McDonald, 2021 ).

The success of an EEW system relies on the end-users, such as the general population, accepting and reacting appropriately to the system and its warnings ( Minson et al., 2018 ). Thus, as EEW systems become increasingly available and transboundary, there is also an ever-increasing need to understand the social aspects of effective EEW systems, their design, development, implementation and use. In this paper, investigation of social aspects means considering factors from various branches of the social sciences including, but not limited to, sociology, behavioral science, psychology, geography, law, economics, and communication. This paper seeks to contribute to the current discourse on EEW by reviewing the literature and the state of research on the social facets involved in EEW systems. This literature review starts with the broad question: “What research has been conducted on the social aspects of earthquake early warning systems?”

This paper is structured as follows. Section Background on earthquake early warning systems contextualizes the review by providing a background to the study, briefly discussing EEW concepts and EEW in the context of broader warning systems. Section Method outlines the methodology. Findings from the literature review are presented in Section Findings. The discussion (Section Discussion and conclusion) examines the findings regarding current and future social research trends for EEW and concludes with a summary of recommendations for future research.

Background on earthquake early warning systems

EEW systems provide real-time information about ongoing earthquakes. Based on two primary concepts, information about earthquakes can be supplied ahead of ground shaking. First, information can travel faster than seismic waves ( Cremen and Galasso, 2020 ). Second, different types of seismic waves travel at various speeds. The P-waves (primary waves) travel fastest, but the damaging energy from an earthquake usually comes from S-waves (secondary waves) and surface waves, and for locations far from the epicenter, they arrive much later than P-waves ( Cremen and Galasso, 2020 ). EEW systems use these concepts to warn users at a distance of incoming ground shaking. People can take protective action, and automated systems can execute pre-programmed responses before the damaging ground shaking arrives ( Allen and Melgar, 2019 ). Timely warnings and appropriate responses can potentially reduce injuries and damage to property ( Allen and Melgar, 2019 ) and help with people's psychological preparedness for ground shaking ( Nakayachi et al., 2019 ).

Traditional EEW systems rely on fixed sensors with configurations that are based on regional systems, on-site systems, or a hybrid of the two ( Cremen and Galasso, 2020 ). Regional (or network-based) systems have dense seismic networks where an array of sensors is deployed in areas with high seismicity potential. The system's warning window starts when the first wave is detected at a source point. The network sends warning to target areas further afield; it allows several tens of seconds of warning depending on the distance between the source and the target sites ( Zollo and Lancieri, 2007 ). On the other hand, on-site systems have sensors instrumented at a single station. The lead time for the warning is estimated using parameters from a few seconds of recorded P-waves on the station's location to predict the ground motion for S-waves or surface waves ( Bindi et al., 2015 ). An EEW system can also be a hybrid of the two; for example, California and Taiwan have hybrid systems ( Wu et al., 2019 ). In recent years, another aspect of EEW research has been conducted on systems that are not based on fixed sensors but instead rely on mobile sensors (e.g., using people's smartphones). Crowdsourced EEW is an evolving domain where EEW systems utilize the participation of people and use mobile and low-cost technologies (e.g., accelerometers of mobile phones) and send warnings through apps or programs built into the mobile's operating systems. Examples of crowdsourced EEW systems are the Earthquake Network ( Finazzi, 2020 ), MyShake ( Allen et al., 2019 ), and the Android Earthquake Alerting System ( Cardno, 2020 ).

UNISDR (2005) and UNDRR (2015) priorities in developing and implementing people-centered early warnings as integral to disaster risk reduction. EEW systems resemble other forecast and warning systems for other natural hazards. These warning systems need to have robust scientific and technical bases, and they must also have a strong focus on the people at risk and have an approach that incorporates all of the relevant risk factors, such as understanding social vulnerabilities and short-term and long-term social processes ( Basher et al., 2006 ). Similarly, an effective EEW system relies not solely on the reliability and accuracy of technological capabilities and processes but also on its embeddedness with human and social systems ( Dunn et al., 2016 ; Velazquez et al., 2020 ).

EEW systems, however, have unique challenges compared to other warning systems. Due to the physical processes of an earthquake, EEW can only commence once an earthquake rupture has started. Thus, EEW systems can only give short warning times of up to several tens of seconds, in contrast to other hazards, such as weather or tsunami warnings, for which warnings can come days, hours, or a few minutes before the events occur ( Strauss and Allen, 2016 ). The short period also implies a high degree of automated processing and near-instantaneous warning, which does not afford time for further human validation ( Gasparini et al., 2011 ; McBride et al., 2020 ). The nature of short warning time impacts how EEW systems are designed to effectively communicate the hazard ( McBride et al., 2020 ) and how people and automated systems respond and make decisions ( Velazquez et al., 2020 ).

EEW generally follows the “Goldilocks principle” ( Cochran and Husker, 2019 ). Too far from the earthquake rupture, warnings can become more accurate and lead times longer, but the intensity of shaking is weak and not dangerous. Too close to the rupture, intensity is expected to be more dangerous, but little to no advanced warning may be sent out. Furthermore, predicting impending ground shaking is still an ongoing scientific feat, with multiple methods still being developed and refined (e.g., Hoshiba, 2021 ). Thus, EEW systems inevitably will have false and missed alerts from the perspective of their end-users. False alerts occur when alerts are issued but the user does not observe the expected ground motion; missed alerts occur when ground shaking is felt but no alert is received ( Minson et al., 2018 ; McBride et al., 2020 ). Challenges for EEW systems include controlling false and missed alerts, managing expectations, and communicating about the uncertainties and limitations of EEW.

Research advances on the social aspects of EEW are still relatively young. One recent study is Velazquez et al. (2020) state-of-the-art review of the technical and socio-organizational components of EEW. The review covered selected established EEW systems–Italy, United States (U.S.) West Coast, Japan, and Mexico–where it was concluded that although there has been increased awareness of people-centered EEW systems, multi- and cross-disciplinary research on EEW remains relatively unexplored. However, Velazquez et al. (2020) review only covered existing EEW systems and did not include those under exploration, planning, and implementation. Further research is needed to understand the social processes and interactions when establishing EEW systems. This systematic review contributes to the literature as it investigates EEW more broadly. It covers not only established systems but includes research papers that are exploratory and projected toward future EEW systems. This review provides an overview of past research and explores future directions for social EEW research in the context of evolving environments.

The literature review method followed the scoping review process defined by Arksey and O'Malley (2005) . Scoping reviews, also known as mapping studies, frame the nature of existing literature on a particular topic ( Kitchenham et al., 2011 ; Par et al., 2015 ); in this study's case, to frame the social science research of earthquake early warning literature. The scoping review starts at a broad level, frames a research trend, and develops inclusion/exclusion criteria to scope a particular topic ( Kitchenham et al., 2011 ; Par et al., 2015 ).

This study started by defining a broad research question: “What research has been conducted on the social aspects of earthquake early warning systems?” Then relevant studies were identified by conducting a literature search using the Scopus database to ensure coverage of significant publications on EEW systems. The scope of the review only includes papers published until September 2021–the time the search was conducted. Table 1 summarizes the search and selection of relevant studies for this literature review. Only peer-reviewed manuscripts were considered. As the researchers have fluency in English and Chinese, manuscripts in both languages were included in the review. A keyword search was used to filter for relevant manuscripts. The search criteria included the term “earthquake early warning” combined with a set of keywords to cover social aspects such as social, behavio * (behavior, behavior, and other variants), perce * (perception, perceptions, and other variants), accept * (acceptance, acceptable, and other variants), user, people, community , and public . The initial search resulted in 365 documents. After the removal of 124 duplicates, a total of 241 manuscripts remained.

Table 1 . Literature search results.

The 241 manuscript abstracts were reviewed and subjected to inclusion and exclusion criteria. Technically focused papers that did not discuss any social aspects of EEW systems were excluded. Examples of exclusions were: papers with abstracts focused solely on algorithms; magnitude characterization; prediction models or methods; network infrastructure; sensors; routing protocols; automated structural response; use case of EEW to infrastructure (dams, buildings); simulations; artificial intelligence; and machine learning. Manuscripts included had abstracts that discussed stakeholder collaboration, public perceptions, user tolerance, user acceptance, user requirements, community impacts, social benefits and challenges, the potential use of EEW for communities, public education, risk reduction, behavior response, and similar themes. Seventy manuscripts (68 in English and 2 in Chinese) were considered for the review after the inclusions-exclusion criteria.

A limitation to this exclusion-inclusion method is that only the abstracts' contents were considered for filtering out the articles. Some articles may have been dropped even if they had social science components in the body but may not have explicitly mentioned those aspects in their abstract. Consequently, technical papers (e.g., Cua and Heaton, 2007 ; Böse and Heaton, 2010 ) were picked up because their abstracts contained a reference to user perspectives (e.g., user-specificity, communication to users, or subscriber's perspective). Despite the technical focus on algorithms of such papers, the qualitative analysis investigated the sections that discussed social or user standpoints.

The 70 articles were subjected to qualitative analysis using thematic coding (as per Flick, 2018 ). Two of the authors conducted the analysis. The thematic coding process involves sequentially building the case summaries for each article, where the manuscript details are organized according to themes ( Flick, 2018 ). To answer the main research question, the case summaries for each manuscript were built around these three base sub-questions:

• What social aspects of earthquake early warning systems are discussed in this article?

• Does the article discuss end-users and broader societal acceptance, use, and perspectives of EEW systems?

• Does the article discuss collaboration between different stakeholders and decision-makers on the design and development of EEW systems?

The thematic analysis used these questions but was also reflexive in gathering other insights into themes. The identified themes were then continuously re-checked and modified after analyzing each case, with this process repeated for each manuscript ( Flick, 2018 ). The findings of the qualitative analysis provided insights into what has been investigated in social research of EEW systems.

Summary of the papers

The 70 manuscripts included in this review primarily discussed or had a significant portion of the paper that discussed the social components of earthquake early warning.

Most of the papers included in this literature review were published from 2007 onwards−20 in the last 2 years (2020 and 2021). Figure 1 illustrates the number of articles included per year. Only one paper from the literature search was published before 2000. Goltz and Flores (1997) paper was on public policy and behavioral response to Mexico's Sistema de Alerta Sismica – one of the earliest EEW systems that issued alerts to the public EEW, initiated in 1989 and completed in 1991.

Figure 1 . Number of published articles included in this review per year.

The articles from 2007 to early 2011 concentrated more on EEW algorithms and relating them to user-specific decision-making (e.g., Cua and Heaton, 2007 ; Böse and Heaton, 2010 ), future application prospects ( Iervolino et al., 2007 ; Kamigaichi et al., 2009 ), and estimation of people's willingness to pay for a hypothetical EEW ( Asgary et al., 2007 ).

After the 2011 Tohoku-oki earthquake and tsunami event, several publications included in this review looked into Japan's EEW performance ( Ritsema et al., 2012 ; Fujinawa and Noda, 2013 ; Ohara and Tanaka, 2013 ; Hoshiba, 2014 ). Also, after 2011, as evidenced by the surge in academic publications from different parts of the world on EEW, more countries and territories were exploring and implementing EEW. The articles included in this review discussed EEW performance or prospects from various geographical locations (See Figure 2 ), including the U.S. West Coast (14), Japan (11), China (3), Mexico (3), New Zealand (3), Ecuador, India, Iran, Italy, Kazakhstan, Pakistan, Taiwan, and Turkey. Nineteen articles did not specify any location, but the EEW concepts and observations could be applied generically. Five articles investigated the broader European region, while another five discussed or compared EEW systems from multiple locations (e.g., Japan and Italy, etc.).

Figure 2 . Locations of focus for the articles included in this literature review.

The 70 articles focused on varying topics within EEW research, each with its own objectives. See Supplementary materials for the list of articles included in this study and the objectives of each paper. Because of the varying focus of each article, a comprehensive appraisal of the EEW systems' technical performance is not within the scope of the review. However, the paper covers research themes resulting from the analysis of the articles as guided by the research questions. The resulting overarching themes are (1) EEW benefits and challenges, (2) end-users' perceptions, (3) multi-stakeholder involvement, and (4) crowdsourced EEW and its implications. See Table 2 for the summary of these themes; note that each theme is not mutually exclusive from the other. Each theme will be discussed in detail in the succeeding sub-sections.

Table 2 . Research themes of the study.

EEW benefits and challenges–social perspectives

Most articles in this review discussed the implications of having or developing EEW systems ( N = 50), arguing for the benefits and highlighting the associated limitations and challenges of having EEW systems. The following 2 subsections discuss the findings on EEW benefits and challenges.

EEW benefits in the disaster lifecycle

Most of the articles discussed the potential benefits of EEW systems to society. The articles highlighted that the main potential benefit of EEW systems revolves around the ability of people and systems to respond to the alert, thus minimizing harm to life and property. Thirty-eight of the 70 articles mentioned the benefit of taking personal protective action. Twenty-eight of the 70 articles mentioned that systems can benefit from EEW if pre-programmed tasks can be performed to minimize impacts (e.g., slowing down of bullet trains, allowing elevators to stop at the nearest floor). People also see the benefit of EEW to mentally brace themselves for the incoming shaking ( Nakayachi et al., 2019 ). Specialized users can also use EEW for situational awareness when responding to earthquake events. Emergency responders can utilize EEW systems to get quick information that will allow them to improve situation awareness through understanding the disruptions and cascading hazards ( Allen and Melgar, 2019 ). Urban Search and Rescue (USAR) teams can use EEW systems to reduce surprise effects and stop dangerous operations ( Auclair et al., 2021 ). EEW can also prompt people to evacuate buildings ( Wu et al., 2017 ) or evacuate coastal areas in preparation for a tsunami ( Necmioǧlu, 2016 ).

Many of the articles included in this review also discuss EEW systems' potential benefits beyond the immediate response to the warning to other stages of the disaster lifecycle. Table 3 summarizes the potential benefits EEW can provide during various disaster phases. For recovery, EEW systems can be incorporated in protecting critical structures, transport, and lifelines from secondary (e.g., fires) and aftershock effects; protecting infrastructures would help society return to normal after an event ( Gasparini et al., 2011 ). For mitigation, setting up EEW systems would help decision-makers know more about exposure and vulnerability, thus potentially helping play a role in policies managing risks ( Iervolino et al., 2007 ). Mitigation can be applied in managing critical infrastructures using EEW systems. For example, the public might have more confidence in a nuclear facility if they know that it is equipped with an EEW system to minimize risks ( Cauzzi et al., 2016 ). Finally, having an EEW system can also promote a culture of preparedness. Public education regarding the system can encourage people to think about earthquakes and their impacts and prepare for them ( Dunn et al., 2016 ; Allen and Melgar, 2019 ).

Table 3 . Summary of benefits discussed by the articles on having an EEW system through the disaster management phases.

Benefits of EEW can be seen from an economic point of view based on savings or loss reduction–computing potential losses when EEW is implemented and comparing the results with the estimated losses if EEW is not implemented ( Oliveira et al., 2015 ). Some attempts have been made to measure and estimate the benefits. A case example of a semiconductor facility in Miyagi prefecture investing USD 600,000 in retrofitting and EEW automation demonstrates EEW cost savings. The facility had estimated losses of USD 15 million from two moderate earthquakes before implementing earthquake mitigation measures, compared to $200,000 losses after experiencing two similar-sized earthquakes with retrofits and EEW automation ( Strauss and Allen, 2016 ).

However, measuring savings on a broader scale is challenging as not all losses can be measured monetarily, and any projection of losses will be based on a landscape of possibilities ( Oliveira et al., 2015 ; Strauss and Allen, 2016 ). Estimating benefits on a broader scale may work with some indicative assumptions. For example, Strauss and Allen (2016) anticipated that EEW could reduce injuries by more than 50% if everyone acted to drop-cover-and-hold after an alert. The saving is estimated at USD 200 million per year on costs the U.S. government would have to expend to address earthquake-related injuries ( Strauss and Allen, 2016 ). Measuring such benefits should be taken with caution, as it is necessary first to have a clear idea of what can actually be done with a few seconds of warning ( Oliveira et al., 2015 ).

Despite the potential for EEW, the benefits of public alerting make assumptions about people's reactions; there is still limited proof of its actual effectiveness in terms of people's responses ( Nakayachi et al., 2019 ; Cremen and Galasso, 2020 ). Wald (2020) expressed two concerns about EEW on the U.S. West Coast: (1) effective warning times of EEW systems are often less than claimed, and (2) the suggested actions responding to the alerts are more challenging than anticipated and thus not as effective as expected. The short warning times of EEW limit the possibilities for effective response ( Wald, 2020 ). A study shows that despite the successful issuance of EEW alerts in the cases of Gunma and Chiba – Japan, the alerts did not motivate people to take action as recommended by official agencies ( Nakayachi et al., 2019 ). In the same cases, the tangible benefit of EEW from people's perspectives is for mental preparation rather than the suggested and anticipated physical response for personal protection ( Nakayachi et al., 2019 ). Thus, the review shows that despite claims EEW is beneficial, there is still a need to understand the nature of the benefits in-depth. Most of the success metrics for EEW have been on the seismological aspects, but EEW's success should also be scrutinized from the end user's lens ( Cremen and Galasso, 2020 ).

Challenges for public-facing EEW systems

EEW systems are complex as they include both technical and social attributes ( Li and Jia, 2017 ). Implementing EEW comes with financial, political, and sociological challenges ( Allen, 2011 ). The papers reviewed also recognize social challenges in achieving effective EEW systems. Some articles discuss various issues that impede the success of EEW systems. The most commonly identified social challenges were (1) the culture of awareness and preparedness education, (2) users' actions in response to warnings, and (3) implications of alerting errors. There are other challenges identified, but these three were identified most frequently by the articles in the review (See summary in Table 4 ).

Table 4 . Top three social challenges to overcome for effective EEW systems.

Twenty-one articles mention the challenge of creating a culture of awareness about the system and preparedness education. It is widely recognized that installing and operating EEW technology requires substantial investment ( Ahn et al., 2021 ). Still, sometimes the costs of public education campaigns are overlooked. Public education for EEW systems must be accounted for to teach people how to use EEW information ( Allen, 2015 ). For example, in Washington State, USA, people have an appetite for EEW but have low earthquake preparedness ( Bostrom et al., 2018 ). Educational and training programs are needed to develop people's ability to know the appropriate self-protection actions ( Herovic et al., 2020 ; Sutton et al., 2020 ). The designers of Japan's public EEW system recognized that EEW would have very short warning times (up to several tens of seconds). Hence, people need to know the principle, purpose, and technical limits of EEW beforehand to ensure effectiveness without causing unnecessary confusion ( Kamigaichi et al., 2009 ). Nakamura et al. (2011) emphasized the need to educate the public about EEW's limitations and integrate comprehensive earthquake preparedness education. It is essential to avoid overreliance on EEW for disaster prevention. The public must be encouraged to have reasonable self-management for earthquake protection beyond an earthquake warning itself ( Nakamura et al., 2011 ). However, even with awareness and education, intended action may not result in actual behavior ( Becker et al., 2020b ) and may still result in inappropriate actions ( Becker et al., 2020a ).

Eighteen articles highlighted the challenge of understanding how users respond to alerts. These articles discussed whether alerts translated to appropriate user actions. Anticipated mitigating actions to alerts may not materialize as expected. For example, in Japan, Nakayachi et al. (2019) study ( n = 359) showed that more respondents used the alerts to mentally prepare for shaking (25%) than to take physical action of moving nearby to a safe place (7%).

Some responses may be affected by the mode in which EEW is delivered. Alerts can be delivered via different means (e.g., sirens or wireless broadcasts), but often they are delivered in the form of short messages. The short message style might mean that people may feel they are only receiving partial information. Consequently, they may result to milling –looking for additional information or confirmation–before taking protective action ( Goltz and Flores, 1997 ; Sutton et al., 2020 ). Responses may also be affected by personal attributes or experiences; for example, different people may also have different thresholds on the level of shaking that would trigger them to take action ( Minson et al., 2017 ). Despite public training and education, it is uncertain how many people perform the official protective action advice of drop-cover-and-hold upon receiving an alert, as highlighted by literature from the West Coast, USA ( Porter, 2018 ), and the Japanese study by Nakayachi et al. (2019) . In another case study from Japan, a proportion of the people intended to take action during the Mw9.0 Tohoku-oki earthquake but could not because of the short warning time before the arrival of the shaking ( Hoshiba, 2014 ). Some studies also highlight the importance of understanding how long a user needs (e.g., seconds, tens of seconds) to take useful action before shaking begins ( Minson et al., 2019 ).

Sixteen articles discuss the challenge of alerting errors, as they can affect people's perceptions and have broader implications for EEW. One often raised risk is that false alerts may trigger mass panic, which is why systems must be configured to reduce false alerts ( Asgary et al., 2007 ). Due to the technicalities of EEW systems, there is a trade-off between missed alerts and false alerts ( Saunders et al., 2020 ). False alerts occur when alerts are issued, but no shaking follows, while missed alerts occur when shaking happens, but no alerts are issued. An optimized alerting strategy needs to consider community tolerance of these false and missed alerts ( Saunders et al., 2020 ). False alerts can negatively impact trust in the EEW system. McBride et al. (2020) note that the issuer (i.e., alerting agencies) and recipients (i.e., end-users) may have different perceptions and thresholds for false alerts.

Scientists expect that the more educated people are about EEW, the higher the acceptability of information error, blind zones, and false and missed alerts ( Guo et al., 2012 ). In Guo et al. (2012) study in China, a survey with 214 participants from all over China, only 23% of respondents accept information errors. In comparison, in a 2012 questionnaire by the Japan Meteorological Agency ( n = 12,000), Japanese respondents had higher acceptability of errors; a significant proportion of the population (78%) is aware of EEW's shortcomings and understands the possibility of false alarms ( Fujinawa and Noda, 2013 ). The difference between Chinese and Japanese respondents can be attributed to the Japanese being more exposed to and experienced with earthquakes and EEW information ( Guo et al., 2012 ). From multiple EEW experiences, researchers have found that despite false and missed alerts, the public in Japan has some acceptability of alerting inaccuracy. A large proportion (85.6%) of respondents ( n = 3,000) from Ohara and Tanaka (2013) study accept the possibility of missed warnings. Despite false and missed alerts, the majority in Japan – more than 90% in Tohoku ( n = 817) and 80% nationwide ( n = 2,000) in Hoshiba (2014) study feel that EEW is useful.

Furthermore, there are situations where multiple EEW issuers are at play (e.g., government authorities vs. private companies in Mexico). One party's false or missed alerts can reduce trust in EEW in general ( Reddy, 2020 ). Liability questions arise on who should send the alerts and who is responsible for false or missed alerts ( Gasparini et al., 2011 ). If false or missed alarms are poorly handled, it can cause chaos and financial loss; therefore, a sound legal framework must be considered for EEW effectiveness and accountability ( Li and Jia, 2017 ). In this regard, only six of the 70 articles mention the legal aspects of EEW. This is an area ripe for further research.

End-user perceptions

A proportion of the articles ( N = 36) include in their discussion an investigation of end-users' and broader societal acceptance, use, and perspectives of EEW. EEW systems have various end-users, including advanced users and the public.

Advanced users' perceptions

Advanced users (i.e., not the public), such as governmental agencies or industrial users, use EEW information for decisions that often have broader implications that may impact society and infrastructure. Advanced users have different contexts for decision-making. For example, a nuclear facilities manager might need to decide whether to shut down a reactor, emergency managers might use EEW information to deploy resources for emergency response, and urban search and rescue teams may decide whether to stop or continue rescue. Advanced users will have different views, depending on their contexts, on what are meaningful EEW lead times between warning and shaking ( Oliveira et al., 2015 ) and on their tolerance for false or missed alarms ( Le Guenan et al., 2016 ). Oliveira et al. (2015) survey showed that 83% of industry operators think 12 se provides sufficient time to take actions to minimize risk for the facility, while 17% did not feel confident that 12 s is sufficient. Le Guenan et al. (2016) study showed that decision makers' risk behavior affects their tolerance for false alarms. A decision-maker with a risk-neutral attitude can tolerate as many as five false alarms a year, but decision-makers with a more risk-prone attitude can handle more ( Le Guenan et al., 2016 ).

In facilities management, the decision on how EEW is approached depends on the vulnerability of the facility and the costs of inaccuracies of estimated ground shaking ( Böse and Heaton, 2010 ). For example, shutting down a nuclear reactor will be costly and have significant consequences ( Cauzzi et al., 2016 ; Minson et al., 2019 ). Operators would like to know an EEW system's reliability beforehand and the system's propensity for false and missed alarms. The chance of missed and false alarms would need to be weighed with the costs and benefits before EEW can operationally be used for nuclear facilities ( Cauzzi et al., 2016 ).

On the other hand, more tolerant users may prefer to get an earlier warning in other contexts even if they are more likely to receive false alerts. For USAR, teams working in high-risk environments (i.e., in unstable and vulnerable structures) find false alarms tolerable if the EEW system overall benefits the life-safety of the rescuers ( Auclair et al., 2021 ). In a study of USAR personnel, 50.9% of respondents considered false alarms to have a low to very low impact in terms of loss of time and efficiency in USAR operations. However, repeated false alarms rather than isolated ones would affect a USAR team's confidence in a system ( Auclair et al., 2021 ).

Two papers included in this review studied advanced users and quantitatively modeled their risk perceptions and decision-making. Le Guenan et al. (2016) emphasized that a participatory viewpoint is necessary for EEW since such systems can affect many groups, including infrastructure owners and elected officials. Le Guenan et al. (2016 , p. 318) study tried to account for end-user preferences using a ‘combination of multi-attribute utility theory and a Bayesian network for earthquake loss assessment'. Their method looks at the different views on acceptable risks, investigating setting a ground motion threshold for decisions to trigger an alert that would have benefits outweighing costs. Cremen and Galasso (2021) pointed out that while Le Guenan et al. (2016) method accounts for risk tolerance, it only works for binary actions (i.e., to trigger or not trigger an alarm). Cremen and Galasso (2021) then proposed an advanced methodology using a multicriteria decision-making (MCDM) approach coupled with a performance-based earthquake engineering framework incorporating Bayesian real-time seismic hazard analysis. Cremen and Galasso (2021) approach goes beyond binary decisions and enables multiple mitigation actions to be evaluated for various dimensions of uncertain risks. These two papers show that modeling risk-based decision-making will help EEW systems become end-user-driven tools to become more effective in promoting seismic resilience.

Public perceptions

Several studies in this review investigate public perceptions of EEW. Four recurring themes relate to public perceptions of EEW end-users. Generally,

(1) The public has favorable views of EEW.

(2) The public's views and level of support are critical to EEW's success.

(3) People's lived experiences with earthquakes affect their views on EEW.

(4) There are concerns regarding public alerting.

Positive public reception

Despite people's mixed responses to warnings ( Huggins et al., 2021 ), people's perceptions of EEW are positive in areas with operational EEW systems available to the public. Studies in Japan (e.g., Fujinawa and Noda, 2013 ; Ohara and Tanaka, 2013 ; Nakayachi et al., 2019 ) show that the public generally views EEW as useful. Similarly, studies in Mexico ( Santos-Reyes, 2019 ) and West Coast USA ( Saunders et al., 2020 ) show that even with limitations in warning times and shaking thresholds, people deem EEW beneficial. In Taiwan, where EEW sensors are installed in schools, teachers view EEW as a valuable tool for promoting and teaching disaster prevention ( Wu et al., 2017 ).

Public views and support for EEW success

National interest will vary dependent on the context of each country ( Clinton et al., 2016 ). In Europe, at the time of writing, EEW was “not yet a product demanded by the general public or even the scientific community ( Clinton et al., 2016 , p. 2442).” The critical variable for the success of an EEW system is whether the public perceives the indispensability of EEW to keep them safe ( Goltz and Flores, 1997 ). Gaining the public's insights is critical in the early stages of considering or developing EEW. A survey ( n = 3,084) exploring the potential for EEW in New Zealand ( Becker et al., 2020b ) shows a different public perception of EEW compared to Europe. The survey in New Zealand, a seismically active nation, shows that most respondents supported an EEW system, signaling an opportunity to move EEW conversations forward ( Becker et al., 2020b ). Aside from considering public perspectives, the social context in which EEW is being developed should also be understood ( Becker et al., 2020b ).

Furthermore, the U.S. West Coast experience shows the successful spread of ShakeAlert was attributed to local stakeholders gathering support and funding to operationalize EEW at the early stages ( Kohler et al., 2018 ). EEW also requires public funding, at least partially, for which public support is needed ( Ahn et al., 2021 ). Where there may be user-pay models of funding, people's willingness to pay depends on their perceptions of earthquake risks and the level of protection they perceive EEW will provide ( Dunn et al., 2016 ; Ahn et al., 2021 ).

Lived experience affects EEW views

Another recurring theme in public perceptions is that people's lived experiences affect their views on EEW. Ahn et al. (2021) study shows that people with lived experiences of earthquakes also perceive a higher risk of harm from earthquakes, thus influencing their views on EEW's usefulness and willingness to pay for EEW. Similar observations can be inferred from Hoshiba's (2014) paper, where it was observed that Tohoku residents, who were most impacted by the 2011 earthquake, were more likely to view EEW positively compared to the national average. Moreover, after earthquake events, there is heightened awareness and recognition of earthquakes among the public, especially in affected regions ( Fujinawa and Noda, 2013 ; Ohara and Tanaka, 2013 ).

Concerns about public alerting

Despite the generally positive reception from the public about EEW, there are concerns related to the public's perceptions and knowledge of EEW alerts. The examples below show that the public may have misconceptions about EEW and associated information and sources that will impact their perception and trust in EEW, thus potentially delaying them from taking appropriate protective action when alerts are issued.

Not all shaking warrants an alert. The alert parameter for ShakeAlert in Los Angeles (LA) to issue a warning is set at Modified Mercalli Intensity Scale Level four (MMI-IV) or above. Yet, this may not be common knowledge for users. During the 5 July 2019 Mw7.1 Ridgecrest Earthquake, many LA residents felt the earthquake and were left unimpressed when no alert was delivered, even though the intensity in LA was MMI-IV and thus below the delivery threshold ( Saunders et al., 2020 ). Because of public pressure from the perceived ‘poor' performance of the ShakeAlert, the target parameter for the system was lowered for the LA area to MMI-III ( Cochran and Husker, 2019 ; Saunders et al., 2020 ). However, shaking at MMI-III is considered weak where it may not be easily recognizable as an earthquake. Setting the system's threshold at this level will pose a different challenge; people may then receive an EEW alert but not feel or recognize the earthquake–which may lead to a perception of false alerts ( Cochran and Husker, 2019 ; Saunders et al., 2020 ).

There may also be pre-conceived notions about earthquake alerts that may not necessarily be helpful. For example, in Mexico City, residents believe that an alert would always give them at least 60-s of warning before shaking arrives ( Santos-Reyes, 2019 ). This belief is partly because of how the Seismic Alert System of Mexico (SASMEX) was designed from the Guerrero Gap to Mexico City, allowing for a close to 60 s prevention time if the rupture comes from the subduction zone along the Pacific coast. The risk of large earthquakes for Mexico City mainly originates from the Pacific coast, which has resulted in SASMEX issuing alerts with warning times of 60 to 90 s in most felt earthquake events. However, earthquakes in Mexico do not only originate from the Pacific coast, such as the 19 September 2017 Mw7.1 earthquake near Mexico City ( Santos-Reyes, 2019 ). In such a case, confusion among the public can ensue when the system does not provide as much warning time as anticipated ( Santos-Reyes, 2019 ). There should be basic public education on how EEW functions; education should be provided on EEW Systems and seismic hazards ( Santos-Reyes, 2019 ).

The public also may struggle with delineating EEW information to warrant responsive action. Many people did not know the difference between EEW and standard earthquake information ( Fujinawa and Noda, 2013 ). Furthermore, in areas where multiple parties can issue EEW alerts, the public finds it difficult to differentiate the authorities from other players ( Reddy, 2020 ).

Multi-stakeholder involvement

Although many of the papers included in this review focused on EEW end-users, some articles ( N = 25) also covered different stakeholders' involvement in the design, development, and deployment of EEW systems. The stakeholders may also be advanced end-users but play a role in influencing the design and use of EEW systems. The findings show that multiple players are involved in EEW conversations. Table 5 summarizes the various stakeholders mentioned by the articles and shows the frequency of articles that refer to them.

Table 5 . EEW Stakeholders identified by the articles.

EEW involves a multi-disciplinary effort. Research is not only conducted by seismologists and physical scientists, and cooperation is needed for the various stakeholders involved in the design, development, and implementation of EEW. For example, Parolai et al. (2018) emphasized the need for better cooperation between seismologists and engineers to deliver better EEW applications. Technology experts are also needed for the technological factors of the software and hardware interfaces of EEW systems ( Goltz and Flores, 1997 ; Minson et al., 2015 ). Collaboration with social scientists is crucial in optimizing public warning systems ( Allen and Melgar, 2019 ; Minson et al., 2020 ). McBride et al. (2020) showcased the value of an interdisciplinary working group that allowed the development of best practices in post-EEW alert messaging.

EEW collaboration also means working across borders with different seismic networks and research groups. In Europe, the project REAKT brought about a consortium of EEW researchers from seismic networks and research groups in the region ( Oliveira et al., 2015 ). Because of the limited capabilities of smaller seismic networks, building effective EEW in Europe will require coordination and sharing of resources in the community ( Gasparini et al., 2011 ). Similarly, for ShakeAlert to work across different states in the U.S., it needs to leverage the Advanced National Seismic System, a federation of cooperating seismic networks throughout the nation ( Kohler et al., 2018 ). Developing an earthquake and tsunami monitoring network and an exploratory EEW system in Central America also saw invaluable data exchange and cooperation across borders between seismological institutions in Central America and Japan ( Strauch et al., 2018 ).

EEW is not purely a research endeavor. Its effectiveness in society also requires close collaboration with various practitioner-based sectors. Earnest partnership between government agencies, policymakers, telecommunication operators, and researchers is indispensable for implementing warning systems ( Malik and Cruickshank, 2014 ). The emergency management sector and communications specialists also play vital roles for EEW in ensuring public safety through appropriate messaging and educational strategies ( Allen et al., 2019 ). EEW conversation must also include the private sector. In some locations, such as Mexico, commercial entities can issue EEW alerts alongside official agencies ( Reddy, 2020 ). There also should be a good relationship between the officials and private providers to avoid confusion with end-users ( Reddy, 2020 ). Furthermore, as advancements in technology allow smartphone devices for crowdsourced EEW, cooperation is crucial with device manufacturers to adapt to technological changes and commercial demands ( Minson et al., 2015 ).

The findings show that aside from end-users, multiple stakeholders are involved in the various stages and processes of EEW systems. This implies that research on the social aspects of EEW should not be limited to downstream alerting and post-alerting communication to the public. It must also investigate the multi-stakeholder and interdisciplinary social dynamics in the design, development, and implementation of EEW systems.

Crowdsourced EEW and its implications

A recurring theme, especially in the more recently published articles, is the concept of crowdsourced EEW. Crowdsourced EEW is a developing area where EEW systems utilize the distributed participation of people and use mobile or low-cost technologies (e.g., smartphones or portable sensors). Nine articles included in this review have revealed advancements in EEW in using portable sensors and mobile devices (e.g., laptops or smartphones) for crowdsourcing EEW. Community-owned commercial or off-the-shelf devices have been recognized as powerful resources for sensor networks ( Faulkner et al., 2011 ). In addition to these community-owned sensors, the ubiquity of mobile devices has expanded the scale of crowdsourced EEW in recent years, as networks can potentially use data from consumers' smartphones rather than solely relying on installed sensors ( Minson et al., 2015 ).

The review has shown that the social challenges to crowdsourced systems include (1) public participation and user retention and (2) liability issues, and (3) commercial demands and ramifications.

Public participation in crowdsourcing

For some of these crowdsourced EEW systems, public participation is necessary. Users need to download an app and register their phones to become sensors in the network and receive warnings ( Allen et al., 2019 ). Example of such system includes MyShake ( Kong et al., 2019 ) and the Earthquake Network project ( Finazzi, 2020 ). One of the challenges for opt-in systems is user retention ( Finazzi, 2020 ). Such systems need to consider how they can keep users interested in installing and keeping the apps on their phones ( Allen et al., 2019 ). EEW systems should find ways to incentivize users to contribute to crowdsourcing efforts (i.e., not uninstalling the app) ( Panizzi, 2016 ). Smartphone app design should consider user interaction as customer satisfaction becomes crucial. For example, how the app consumes energy directly relates to satisfaction ( Zambrano et al., 2017 ).

Liability concerns

EEW, whether crowdsourced or official, has not been fully utilized in many parts of the world because of liability issues; emergency managers are reluctant to automate EEW because of accountability in case of false or missed alarms ( Gasparini et al., 2011 ). For crowdsourced EEW, it also becomes a blur on who is responsible for false or missed detections ( Finazzi, 2020 ). Moreover, privacy and data protection must also be considered when handling user location information for crowdsourced systems ( Finazzi, 2020 ).

The existence of official, crowdsourced, and privately-run EEW can confuse matters. Multiple parties, official and non-official, can issue alerts, but the public cannot usually distinguish between them ( Reddy, 2020 ). Sometimes, alerts from different sources are also not delivered to their intended recipients, and one party's false or missed alerts can reduce public trust in EEW as a whole ( Reddy, 2020 ). There may also be no barriers limiting competing parties from sending intentional false alerts to subdue competition ( Reddy, 2020 ). Such liability considerations and issues impede EEW progress ( Finazzi, 2020 ).

Commercial demands and implication

Finally, the use of smartphones for EEW comes with the pressure to keep up with commercial demands ( Minson et al., 2015 ). Using smartphones provides opportunities for crowdsourced EEW systems, as they do not need significant capital outlays for equipment ( Minson et al., 2015 ). However, this also means that crowdsourced EEW systems should align and keep up with the multiple existing mobile operating systems and their levels of permission access to data ( Minson et al., 2015 ; Zambrano et al., 2017 ). Minson et al. (2015) also point out that the objectives of crowdsourced EEW systems might not align with the commercial intent of smartphone devices. Any implementation issues may have ramifications for the commercial products.

Discussion and conclusion

The results and subsequent discussion have several limitations that must be acknowledged. The interpretation of results is limited to the 70 papers written in English and Chinese texts found in the Scopus database. The research gaps identified herein are within the context of these 70 papers. Therefore, there may be papers or subject areas unexplored. Additionally, EEW is a rapidly evolving field of study, and there will inevitably be papers published since September 2021 that were not included in this review (e.g., Becker et al., 2022 ; Fallou et al., 2022 ; McBride et al., 2022 ; Vaiciulyte et al., 2022 ). Future research should consider expanding the literature coverage by including different databases and more recent publications. The focal point of this paper is to determine the extent of research thus far on the social aspects of earthquake early warning.

The 70 articles have touched on a breadth of social science research topics. However, multiple gaps still exist in investigating the social aspects of EEW. Three fundamental areas to further investigate: (1) understanding EEW effectiveness from the social standpoint, (2) uncovering integrated multi-stakeholder approaches throughout the disaster lifecycle and the EEW design cycle, and (3) understanding how EEW and society adapt to innovations and changes–including legal perspectives.

EEW effectiveness

The effectiveness of EEW systems has been measured from seismological and technological standpoints. They can be evaluated on the accuracy and timeliness of ground motion estimates ( Meier, 2017 ) or using the latency of alert time and lead time ( Kamigaichi et al., 2009 ; Minson et al., 2018 ). An economic valuation can also estimate effectiveness by measuring the estimated loss reduction in relation to investment ( Oliveira et al., 2015 ). From the human behavior perspective, the view of effectiveness is in how end-users recognize, interpret, and respond to EEW ( Wald, 2020 ).

End-users' reactions to warnings are crucial to EEW systems' effectiveness in society. However, twenty of the papers in this review presumed that EEW would provide benefits (e.g., individuals will use the lead time to drop-cover-and-hold). However, those that reported the actual outcomes of EEW, such as in the Japanese contexts, indicated that fewer than the expected number of people took the prescribed protective action. As Nakayachi et al. (2019) indicated, despite numerous indications of the potential utility of EEW, there is limited evidence of the actual (not potential) benefits of warnings to the public. Research thus far, to some extent, has relied on the potential benefits of EEW ( Wald, 2020 ). Future EEW research must operate beyond these assumed benefits and should work with realistic representations of the EEW benefits to society. Further investigation is needed on the actual effectiveness of EEW from a social standpoint.

It must be acknowledged that gathering data for people's actual reactions can be challenging, as people's response to an earthquake is dependent on the specific conditions that it is difficult to compare across earthquake events. Furthermore, it is hard to compare groups of people (who got the warning to those who did not) in a particular situation. Therefore, the usual way, so far, to gather such data is through surveys that require respondents' introspection. Future studies should investigate improving the data gathering methods and finding innovative ways to capture end-user perspectives on EEW effectiveness (e.g., earthquake simulation or analysis of alternative data such as CCTV or social media).

More importantly, researchers should investigate why there are low numbers of people taking protective action with EEW. A recent study in Peru ( n = 2,625) confirms the past studies' findings that most alert recipients do not take protective action ( Fallou et al., 2022 ). To improve the effectiveness of EEW, more study is required to understand why action is taken (or not) and how to motivate more people to take appropriate protective action. A people-centered EEW means building social capacity in people's disaster risk knowledge and their ability to respond to warnings appropriately. People-centered EEW also challenges system designers and researchers to consider the heterogeneity of end-users. Different groups' accessibility to the system (for example, the elderly and differently-abled) should be considered. More research is needed to understand people's experience, knowledge, and capability to respond to the alerts.

Involvement throughout the disaster management lifecycle and EEW design cycle

This study has shown that most social research on EEW has focused on the response stage of the disaster management phase. However, the articles have also revealed that people also interact with EEW in the other phases of disaster management. Further research should explore EEW's role beyond the response stage of the disaster lifecycle. Particularly, EEW can be used to promote earthquake preparedness and create a culture of earthquake awareness and readiness. Improving risk communication pre-crisis and throughout the earthquake crisis lifecycle could potentially improve EEW's overall effectiveness ( Herovic et al., 2020 ). For EEW, pre-crisis education could provide (a) information about the potential for earthquakes, EEW and its limitations, and possible impacts on the community, (b) how to prepare, and (c) campaigns about appropriate self-protection actions during earthquakes in general and when receiving alerts ( Becker et al., 2020a ; Herovic et al., 2020 ). Future research should investigate integrating EEW public education across the disaster management phases of recovery, mitigation, and preparedness to improve earthquake resilience. Another area for research investigation is the design, implementation, improvement, and evaluation of the EEW education programs toward the overall effectiveness of EEW and earthquake resilience of communities. More research could expand on the best practices for EEW public education, considering different types of users and their context of use for EEW.

Any disaster risk reduction effort needs to incorporate awareness, education, training, and collaboration mechanisms ( Malik and Cruickshank, 2014 ). Research on EEW should not focus only on communicating to end-users but also needs to investigate the interactions between various entities involved in the EEW design process. EEW research often involves a design science process–where the design of a solution (i.e., EEW system) also produces generalizable knowledge that can be appropriate to a research community ( Johannesson and Perjons, 2014 ). Creating an EEW system requires strong foundations in the technical knowledge base. Still, for EEW to be effective, it must also be appropriate to its application domain (i.e., relevant to its stakeholders). Implementing EEW suitable for society will require engagement with multiple stakeholders throughout the process, including the public, scientific experts, and sectoral and industrial partners. A collaborative framework is needed to engage EEW research and practice. Tan et al. (2021) formed a community of practice for earthquake early warning discussions in New Zealand; the collaborative framework shows the value of diversity of perspectives to enhance knowledge exchange toward developing an EEW system. Future research should investigate integrated stakeholder approaches for advancing EEW. Research is also needed to enhance communication and collaborations for EEW researchers and stakeholders across disciplines throughout the system design, development and implementation.

Social EEW research should adapt to the fast-changing trends

With innovation in technologies, many opportunities arise for EEW design and implementation. This review has shown that smartphones are now being used for crowdsourced EEW. The ubiquity of smartphones means that EEW is becoming transboundary. EEW design and development are no longer limited to geographical jurisdiction and can be implemented across borders. For example, the Google initiative introduced the Android Earthquake Alerts System in New Zealand and Greece in April 2021 ( Voosen, 2021 ). This also raises the concern of EEW players' civic responsibility, and a step further is the concern of legal liability. As of writing, minimal research has focused on the legal aspects of EEW systems. Articles in this review may have included some legal topics in their discussion, but only two articles ( Li and Jia, 2017 ; Valbonesi, 2021 ) in this study focused primarily on the legislative components of EEW. But with the changing contexts due to technological trends, evidenced-based research is needed to inform regulation, policy, and planning of effective EEW in countries and territories.

Multiple countries and territories now have official EEW systems. Still, most of those capable of having official operational EEW are high-income countries/territories (e.g., Japan, West Coast USA, Taiwan etc.). These EEW systems are costly to deploy, implement, and maintain ( Given et al., 2014 ; Prasanna et al., 2022 ). Because of high costs, lower-income countries have not had the same opportunity to access EEW as an earthquake mitigation tool. However, low-cost alternative technological solutions, such as using micro-electromechanical systems (MEMS) ( Cochran et al., 2011 ), smartphones and apps ( Cardno, 2020 ; Bossu et al., 2022 ), and decentralized architectural networks ( Prasanna et al., 2022 ) can make EEW more accessible to lower-income countries. Future social science research should investigate how these low-cost technological solutions will be utilized by various countries (e.g., high-income and low-income) as mitigation tools. Social challenges arising from low-cost solutions should also be monitored and investigated.

Low-cost alternatives such as smartphones and other low-cost devices for crowdsourced EEW imply that more players can issue EEW alerts. While more options can generate benefits, they can also create problems. As in the case in Mexico, a false alert issued by an independent app caused confusion, created concerns for the official authority and raised the question of what civic responsibility might mean for people behind EEW systems ( Reddy, 2020 ). Technological changes bring about new ways to design and implement EEW systems, and it also changes end-users perspectives. EEW research would need to reassess and update knowledge and assumptions as it applies to new and changing contexts.

Across the world, EEW systems already exist, and more countries are considering designing and implementing EEW for earthquake resilience. The rapid development of technologies and methods has provided a deeper physical understanding of earthquakes and improved the EEW processes for better warnings. As EEW innovates further and becomes more accessible and transboundary, social science research must also take a proactive role in the research advances of EEW, including legal perspectives.

This paper addresses the social science knowledge gap on EEW by reviewing the literature. Each of the 70 articles included in this review had different objectives, but collectively they have provided insight into the social science research relating to EEW systems. The articles in this review look at EEW from different perspectives, such as advanced end-users, the public, and the various EEW stakeholders. The findings reiterate that public education is critical for effective warning systems. The articles show that despite the various potential benefits of EEW to society, there is still a further need to understand EEW's impacts and interactions with society.

Social research in EEW is not just about delivering alerts to end-users. Social science research is needed to improve EEW systems further; in understanding how people, stakeholders and end-users, interact with EEW throughout its development process and when implemented through the various phases of disaster management. Suggested topics for future research include (1) advancing our understanding of why people take action or not and ways to encourage appropriate action when alerted with EEW, (2) enhancing public education – best practices for communicating, educating, and engaging with the public about EEW and earthquake resilience, and (3) keeping up with technology advances and societal changes, investigating how these changes impact how EEW interacts with society from various standpoints including legal perspectives.

Author contributions

Conceptualization and funding acquisition: MT, RP, JB, KS, AB, CK, and EL. Methodology, writing—original draft preparation, and visualization: MT. Formal analysis: MT and AC. Investigation: MT, JB, KS, RP, and AC. Writing—review and editing: MT, JB, KS, RP, AB, CK, AC, and EL. Project administration: MT and RP. All authors have read the manuscript agree to be accountable for the content of the work.

This research was funded by Massey University Strategic Investment Fund 2020 and by the Toka Tū Ake EQC, New Zealand: Project No. 20794.

Conflict of interest

Author EL is employed by Sysdoc Ltd., Wellington, New Zealand.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2022.939242/full#supplementary-material

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Keywords: earthquake early warning, social science, warning systems, literature review, earthquake resilience

Citation: Tan ML, Becker JS, Stock K, Prasanna R, Brown A, Kenney C, Cui A and Lambie E (2022) Understanding the social aspects of earthquake early warning: A literature review. Front. Commun. 7:939242. doi: 10.3389/fcomm.2022.939242

Received: 08 May 2022; Accepted: 29 July 2022; Published: 24 August 2022.

Reviewed by:

Copyright © 2022 Tan, Becker, Stock, Prasanna, Brown, Kenney, Cui and Lambie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marion Lara Tan, m.l.tan@massey.ac.nz

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

How Do Earthquake Early Warning Systems Work?

Earthquake early warning systems don’t predict earthquakes. Instead, they detect ground motion as soon as an earthquake begins and quickly send alerts that a tremor is on its way, giving people crucial seconds to prepare.

In 1985, Caltech seismologist Tom Heaton published the first paper on the concept of earthquake early warning systems, networks of ground-based sensors that send alerts to users when the earth begins to tremble.

Such systems, including ShakeAlert on the West Coast of the United States, operate on the principle that while seismic waves travel at just a few miles per second, electronic alerts from the region of the epicenter can be sent almost instantly. Here's how it works:

• During an earthquake, several types of seismic waves radiate out from the quake's epicenter. First, weaker but faster-moving P-waves trigger sensors that, in turn, transmit signals to data processing centers.
• Algorithms quickly estimate the earthquake's location, magnitude, and intensity: Where is it? How big is it? Who is going to feel it?
• The system then sends an alert before slower, but more destructive S-waves and surface waves arrive.

Although people who are near the epicenter will have little, if any advance warning, those farther away may have critical seconds to brace for shaking. Paired with automated responses that can slow trains or shut off gas lines, early warning systems may help prevent some of the injuries and damage typically associated with major quakes.

An earthquake just happened. Why didn't I receive an alert on my smartphone?

• You might be too close to the epicenter . Early-warning alerts are typically delivered three to five seconds after an earthquake starts. That's the time it takes for seismic waves to travel to the closest stations, and for computers to analyze the data. If you are less than 10 miles from the epicenter, it is unlikely you will get a warning.
• The shaking might not have been strong enough . It is important to remember that most people experience weak shaking during an earthquake. This is because large earthquakes are rare, and because individuals are often too far from the epicenter to experience significant shaking. Currently, apps, such as MyShake , are designed to send alerts only for stronger shaking. This could change as scientists and public officials continue to tune the system's parameters.

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Earthquake Early Warning - Overview

• Publications

USGS is actively pursuing research in earthquake early warning.

How the ShakeAlert® System Works

DETECT, DELIVER, PROTECT:  ShakeAlert ® is not earthquake prediction. Rather, the USGS-operated ShakeAlert Earthquake Early Warning system detects an earthquake that has already started and estimates its location, magnitude and shaking intensity. If an earthquake becomes large enough to meet USGS alert thresholds, a ShakeAlert Message is issued. Then, technical partners, which have entered into a license agreement with the USGS, use this information produce and deliver an alert that prompts people to take a protective action, such as DROP, COVER, AND HOLD ON, and/or to trigger an automated action that can protect vital systems, equipment, facilities, and infrastructure. These automated actions could include slowing a train, closing valves, issuing a public announcement, and many others.

The ShakeAlert system takes a network approach to earthquake detection and alerting. This network uses sensors distributed over a wide area where earthquakes are likely to occur on the West Coast of the United States (with nearly 1,700 anticipated on network build-out). Data from individual sensors across large regions are combined to maximize accuracy and alerting time during moderate-to-large earthquakes.

During an earthquake, a rupturing fault produces several different kinds of waves that carry energy away

from the epicenter like ripples from a rock thrown into a pond. The fastest-moving seismic waves (primary or P-waves) travel about 3.7 miles per second and generally do not produce strong shaking.       P-waves are followed by slower moving, and generally more damaging waves (secondary or S-waves) and surface waves that travel about 2.5 miles per second. The ShakeAlert sensor network detects earthquakes quickly, and immediately transmits data to a ShakeAlert Processing Center, where estimates of the location, size, and shaking intensity of the earthquake are determined in a matter of seconds.

Technical Partners are integral to the ShakeAlert System because they are responsible for producing and delivering ShakeAlert-powered alerts to people and critical systems. Technical Partners span multiple industries and sectors, and include private for-profit companies, public entities, and nonprofits that can benefit from becoming part of the ShakeAlert System.

ShakeAlert works because:

• P-waves travel almost twice as fast as the damaging S-waves and surface waves; and
• The speeds of today’s data telecommunications systems are many times faster than seismic waves. Both of these factors make it possible for ShakeAlert-powered alerts to reach people before shaking arrives. Because of the speed difference between P-waves, S-waves, and surface waves, someone who is farther from the earthquake’s origin has more time to potentially receive an alert before shaking arrives.

ShakeAlert®-Powered Alert Delivery Levels

If an earthquake becomes large enough to meet USGS alert thresholds, a ShakeAlert Message is issued.  ShakeAlert technical partners use this information to produce and deliver alerts that rapidly prompts people to take a protective action, such as DROP, COVER, AND HOLD ON, and/or to trigger an automated action that can protect vital systems, equipment, facilities, and infrastructure. These automated actions could include slowing a train, closing valves, issuing a public announcement, and many others.

For example, cell phone app providers and Android can deliver ShakeAlert-powered alerts to people who could feel weak shaking (Modified Mercalli Intensity - MMI III) or greater for earthquakes M4.5 and larger. For people who could feel moderate shaking (MMI V) or greater Android delivers alerts with more urgent language.

The Modified Mercalli Intensity (MMI) Scale is composed of increasing levels of intensity that range from imperceptible shaking to catastrophic destruction; levels of intensity are designated by Roman numerals. The MMI Scale does not have a mathematical basis; instead, it is a holistic ranking based on observed effects. The lower range of the MMI scale generally deals with the manner in which the earthquake is felt by people. The higher range considers observed structural damage.

For More Information:

Earthquake early warning for estimating floor shaking levels of tall buildings, the impact of 3d finite‐fault information on ground‐motion forecasting for earthquake early warning, evidence-based guidelines for protective actions and earthquake early warning systems, expected warning times from the shakealert earthquake early warning system for earthquakes in the pacific northwest, commentary: the role of geodetic algorithms for earthquake early warning in cascadia, earthquake early warning in aotearoa new zealand: a survey of public perspectives to guide warning system development, developing post-alert messaging for shakealert, the earthquake early warning system for the west coast of the united states of america, shakealert earthquake early warning system performance during the 2019 ridgecrest earthquake sequence, the potential of using dynamic strains in earthquake early warning applications, practical limitations of earthquake early warning, earthquake early warning shakealert 2.0: public rollout, estimating rupture dimensions of three major earthquakes in sichuan, china, for early warning and rapid loss estimates, entire u.s. west coast now has access to shakealert® earthquake early warning.

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How do I sign up to receive ShakeAlert®-powered Alerts on my phone? Provider Type Apple Store Google Play Link States USGS/FEMA Wireless Emergency Alerts N/A FEMA | WEA CA/OR/WA MyShake TM Mobile App Y/Y MyShake CA/OR/WA Google Android Operating System N/A Google CA/OR/WA Alert San Diego with ShakeReadySD Mobile App Y/Y Alert San Diego CA The USGS issues ShakeAlert ® Messages but alert delivery...

Can I get on a list to receive an email message when there is an earthquake? How do I sign up for earthquake notifications?  Are there any Feeds I can subscribe to?

Please go to the USGS Earthquake Notification Services (ENS) to sign up for free emails or text messages to your phone. Use the default settings or customize ENS to fit your needs. Also check out the many different Earthquake Feeds . ENS is NOT an earthquake early warning system. Messages issued by ENS will almost always arrive after you would feel any shaking. Learn more: How do I sign up for...

Berkeley Seismology Lab (UC Berkeley)

California department of conservation, california institute of technology (caltech), central washington university, gordon and betty moore foundation, oregon department of geology and minerals industries, oregon military department, office of emergency management (oem), pacific northwest seismic network (pnsn), southern california seismic network (scsn), university of california, berkeley (ucb), university of nevada, reno, university of oregon, university of washington, washington military department, emergency management division, washington state department of natural resources (dnr).

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• Published: 02 September 2024

Can we develop a more targeted approach to mitigating seismic risk?

• Danhua Xin 1 , 2 ,
• Zhenguo Zhang 1 , 2 ,
• Bo Chen 3 ,
• Friedemann Wenzel 4 ,
• Yilong Li 1 &
• Xiaofei Chen 1 , 2  

npj Natural Hazards volume  1 , Article number:  19 ( 2024 ) Cite this article

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• Natural hazards

The recent high death tolls caused by large earthquakes are a further indication that earthquakes remain one of the most destructive natural hazards in the world and can seriously threaten the achievement of disaster reduction goals. To effectively reduce the existing seismic risk, the limited available mitigation resources should be allocated to areas with the most severe potential risk. However, identifying localized concentrations of risk requires detailed studies. Here, we propose a strategy to delineate regional high seismic risk zone at a fine resolution and with high confidence. We demonstrate this strategy by using the seismic hazard and loss estimation results for earthquake scenarios with a magnitude of Mw 7.5 for the Jiaocheng fault of the Shanxi Rift System, China. Our analyses reveal that the delineated zone accounts for only ~7% of the regional land area but for ~85% of the total financial loss. We recommend prioritizing seismic risk mitigation measures in such high-risk zones, especially for densely populated cities in seismically active areas, to better meet the disaster risk reduction targets in the Sendai Framework.

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Introduction.

The high death tolls caused by large earthquakes in the past two decades (the Nepal earthquake in 2015, the Japan earthquake in 2011, the Haiti earthquake in 2010, and the Wenchuan earthquake in 2008, etc.) further indicate that earthquakes are among the most destructive natural hazards in the world 1 , 2 , 3 . With rapid urbanization processes in moderate to large cities, the accumulation of population and wealth has greatly accelerated. Such a large-scale migratory influx requires the rapid construction of new dwellings and infrastructures within a short time, often while sacrificing quality and safety, which inevitably increases the physical vulnerability of exposed structures and thus poses potentially high seismic risk, especially when cities are located within a seismically active zone 1 . The extensive collapse of buildings and the large number of fatalities caused by the recent 2023 Turkey-Syria earthquake further highlight the urgent need to utilize pre-disaster risk mitigation measures, which is critical for achieving the sustainable development goals set by the United Nations and the disaster risk reduction targets outlined in the Sendai Framework 2015–2023 4 , 5 .

Since the 1960s 6 , probabilistic seismic hazard maps have been widely used in various countries and regions to regulate the seismic code of new buildings, but ways to systematically and effectively improve the seismic resilience of existing buildings have not been discussed in depth in the literature. Cost‒benefit analyses have shown that compared with the large cost of emergency response during post-earthquake management, the most effective way to achieve earthquake resilience is to be fully prepared prior to the arrival of an earthquake (e.g., by optimizing disaster risk reduction strategies, retrofitting fragile buildings and infrastructures, educating the public on reasonable earthquake response and execution, etc.) 7 , 8 . However, at all levels of government, the available budgetary resources needed to utilize these risk mitigation measures are always limited, which is generally cited as the major obstacle in any disaster prevention policy 9 . In this context, the pre-identification of the “priority zone”, where the potential seismic damage will be most severe, is necessary since prioritizing seismic risk mitigation actions in these zones can help maximize the efficiency of limited budgets and minimize the loss of life and property in subsequent damaging earthquake events.

When the seismic threat to the target region is mainly from a single fault while information on the likelihood of future earthquakes on this fault is inadequate, to better identify the location of a regional high seismic risk zone, it is crucial to understand the severity of potential earthquake scenarios and their impacts on the social and economic systems, especially for low frequency/high consequence earthquake events providing the upper limit for contingency planning. Thus, assessment of seismic risk must be as accurate as possible given the available information and the associated uncertainties 3 . Seismic risk assessment is based on three layers of information: hazard, exposure, and vulnerability. Hazard refers to the spatial distribution of ground shaking generated by a potential earthquake; exposure includes the attributes of exposed elements to a potential earthquake in terms of value, location, and relative importance (e.g., building, critical facility, and infrastructure); and vulnerability describes the susceptibility of those exposed elements to being damaged in a future earthquake. Each information layer can be determined by using different methods and data inputs. Thus, uncertainties exist in every step of the seismic risk modelling chain. Holistic analyses of the effect of uncertainty on seismic risk in Cologne, Germany (an area with relatively low seismic hazard) showed that the greatest contribution to the total uncertainty in seismic risk was from the hazard part 10 . This conclusion is also supported by several other studies with similar focus 11 , 12 , 13 . For high hazard areas, since the rupture patterns of large earthquakes are more complex, the variation in simulated ground motions is expected to have a large impact on the risk estimation as well, as revealed by studies conducted for Istanbul, Turkey and Thessaloniki, Greece 14 , 15 . In contrast, when the spatial resolution of the exposure model was changed from 1 km × 1 km to 8 km × 8 km, the difference in the estimated seismic loss was less than 5% 16 . Additionally, studies in the field of earthquake engineering have indicated that the uncertainty in building response is more sensitive to changes in ground shaking than to changes in structural modelling 17 , 18 , 19 , 20 . All these studies share the consensus that compared with the changes in exposure and building vulnerability, the variation in seismic hazards has the most notable impact on the final risk estimates. We are fully aware that there are other important uncertainties in risk modelling used for public policy actions, including quality of building inventory data, building collapse modelling, and casualty estimation, etc., but that examining them is not the focus of this paper.

Since for deterministic scenario earthquakes the uncertainty of the ground motion model controls the uncertainty of the risk model 1 , precisely and accurately characterizing the ground motion caused by future earthquakes is the key to determining whether a reliable seismic risk model can be established. Similar to approaches used in other science and engineering research, studies involving predictions of earthquake ground shaking typically begin with empirical formulas, such as ground motion prediction equations (GMPEs), regressed from historical data and observed information 21 . GMPEs are easy to use and can be conveniently adapted to different tectonic environments. Most importantly, they are regressed from actual observations. However, although widely used, empirical GMPEs have shortcomings, such as the intrinsic ergodic assumption 22 , 23 , insufficient consideration of spatial correlations 24 , 25 , 26 and relatively poor constraints in the near-source region of destructive earthquakes due to the sparsity and scarcity of recorded data 14 , 15 , 27 , 28 , 29 . These GMPE shortcomings have limited the accuracy of seismic hazard and risk assessment for areas with densely distributed building portfolios and infrastructures 25 , 26 , 30 , 31 , 32 , 33 .

With advances in high-performance computing and numerical modelling techniques in the past two decades 34 , an alternative ground shaking simulation method, namely the physics-based simulation (PBS) method, has been rapidly developed 35 , 36 and is considered to be a next-generation tool for seismic hazard assessment 14 . Compared with the empirical GMPEs, the PBS method can directly simulate the physical processes of seismic rupture and wave propagation and incorporate the complexity in fault source structures and propagation media; additionally, it can adequately describe the spatial correlations among ground shaking measurements at multiple sites 37 . Furthermore, the resolution of the spatial heterogeneity of ground motion simulated by using the PBS method is higher than that simulated based on GMPEs 14 . Moreover, having PBS-based ground motions can also supplement the earthquake scenarios without data, e.g., large earthquakes that weren’t recorded, or ground motion records in near-fault regions. Due to the advantages of the PBS method, it has recently been embedded in several seismic hazard assessment frameworks 29 , 38 , 39 , 40 . However, despite being rapidly developed, the reliability of PBS-based ground motion simulations is strongly dependent on the robustness of model inputs (e.g., fault geometry, background stress and property of the rock media, and friction law) and different or even contrasting simulation results could be produced when the parameters associated with these inputs are changed 36 , unless for regions with the advantage of having seismic monitoring networks that can collect or provide the input parameters required for PBS in a timely manner. Clearly, each ground shaking simulation method has advantages and disadvantages, as do the corresponding seismic risk assessment models. Efforts have been made to improve the assessment of seismic hazard by integrating the PBS-based predictions with those obtained from an empirical GMPE approach (e.g., the CyberShake Project of Southern California Earthquake Center) 38 , by using PBS simulations to eliminate the ergodic assumption in newly developed GMPEs 41 , or by fostering interdisciplinary research on reducing future losses to extreme events (e.g., the M9 project in the Cascadia area) 42 . However, how to comprehensively combine the results of different seismic risk models into the identification of regional high seismic risk zones is a problem that still requires more in-depth exploration.

The focus of this paper is the proposal of a strategy that can delineate regional high seismic risk zones at fine resolutions and with high confidence. Considering the large impact on the risk estimation due to variation in seismic hazard, the key part of the high-risk zone delineation strategy is to compare the seismic risk calculated from two quite different ground shaking prediction methods. In particular, it is assumed that both exposure and vulnerability are reducible factors of risk, and their uncertainties can be decreased step by step when more information on the exposed elements is available. Therefore, the same exposure and vulnerability information will be combined with different hazard inputs to better reflect the impact on seismic risk due to the variation in seismic hazard. We demonstrate this regional high seismic risk zone delineation strategy by using the seismic hazard and loss estimation results for earthquake scenarios with a magnitude of Mw 7.5 for the Jiaocheng fault of the Shanxi Rift System, China; this magnitude represents the maximum magnitude of an earthquake that has occurred in this region according to historical seismicity and tectonic background. The earthquake scenarios considered are all associated with the Jiaocheng fault of the Shanxi Rift System, China (Fig. 1 ). This region is one of the most seismically active zones in China 43 , 44 . The Jiaocheng fault is in the southwestern part of Taiyuan, which is the capital and largest city of Shanxi Province in northern China, and historically, this region has experienced many earthquakes (Fig. 1 ). Should new damaging earthquakes occur here, millions of people in Taiyuan and its neighbouring area would be seriously affected. Therefore, delineating the regional high seismic risk zone caused by earthquakes from the Jiaocheng fault is essential. We want to emphasize that for other cities located in active seismic zones and with dense populations and fixed assets, the strategy proposed in this paper can also be applied to delineate high seismic risk zones by considering the occurrence of low-frequency/high-consequence earthquakes at neighbouring seismic sources. Such information is crucial for local policy makers to determine how emergency response resources should be deployed and where pre-disaster risk mitigation actions should be prioritized to enhance response and recovery and decrease hazard exposure and vulnerability. Together with building more seismic-resistant structures, these measures are the best preparedness actions that we can take to minimize potential fatalities and financial losses and strengthen the resilience of society before the next destructive earthquake occurs.

a Topographic map. The blue and red arrows represent the directions and relative values of the maximum and minimum horizontal compressive stresses. The red line illustrates the surface trace of the nonplanar Jiaocheng Fault, which extends ∼ 110 km in the strike direction. The whole Jiaocheng Fault is categorized into three main segments (southern, middle, and northern) that have nearly planar geometries and are connected by corners located near Wenshui and Qingxu counties. The four black stars depict the epicentres of the four earthquake scenarios. The white circles are historical earthquakes that occurred in this region, and the three red solid circles have Ms≥7.0. b The 3D geometry of the Jiaocheng Fault. The white stars depict the epicentres of the four earthquake scenarios with a magnitude of ~Mw 7.5 (reproduced from Xin and Zhang 37 ).

In this paper, we develop a series of seismic risk assessment models for earthquake scenarios at the Jiaocheng fault. Since the uncertainty in seismic hazards is considered to have the largest impact on seismic risk assessment and each of the current mainstream ground motion prediction methods (empirical GMPE and the PBS method) has its advantages and disadvantages, the ground shaking maps simulated by both methods for the four earthquake scenarios at the Jiaocheng fault (Fig. 1 ) will be used (Fig. 2 ) as the hazard inputs. The consistency of these ground shaking maps has been discussed in detail in our recent work 37 . Then, by joining the rectified ground shaking maps (Figs. 3 , 4 ) with the high-resolution residential building stock model (Fig. 5 ) and the vulnerability curves of representative building types (Figs. 6 , 7 ) in the case study area, we will develop seismic risk models and evaluate their reliability by comparing modelled seismic losses (Fig. 8 ) with losses estimated from the empirical loss model (Fig. 9 ), which is specifically regressed from historical earthquake damage information for the study area. Finally, the verified seismic risk models will be comprehensively combined to demonstrate how to delineate the regional high seismic risk zone for the case study area (Fig. 10 ). Due to the content organization structure required by the journal, the Methods section is put at the end of this paper. However, for a better understanding of the workflow of this study, the readers are strongly recommended to read the technological details in the Methods section first before diving into the details in the Results and Discussion sections.

a – d The simulated PGA distribution maps for the four earthquake scenarios on the Jiaocheng Fault by using the curved grid finite-difference method (CG-DFM). The black pentacle in each panel represents the epicentre that initiates the rupture process of each scenario earthquake. e – i PGA maps generated by the empirical GMPEs of “BA08” 46 , “CB08” 47 , “BSSA14” 48 and “CB14” 49 .

a The site classes assigned by Li et al. 44 for the Shanxi Rift System according to the definitions in Table 5 . b Digitalized site classification map of the case study area.

a – d The original PGA maps are those shown in Fig. 2a–d for the four earthquake scenarios on the Jiaocheng Fault by using the curved grid finite-difference method (CG-DFM). e – h The original PGA maps are those shown in Fig. 2e–h generated by the empirical GMPEs of “BA08”, “CB08”, “BSSA14” and “CB14”. Note that rectified PGAs are plotted only for the 28808 1 km × 1 km grids in Fig. 5 since they have available exposure information and will be further used for seismic risk calculation in Fig. 8 .

The distribution map of residential building replacement value in each 1 km × 1 km grid developed for the case study area.

a The construction period distribution of surveyed individual buildings in the downtown area of Taiyuan city. b The ratio of surveyed buildings classified by construction periods and storey classes. c The ratio of surveyed buildings classified by construction periods and structure types. It is worth noting that the dividing threshold of each period (namely 1957, 1977, 1990 and 2001) corresponds to the issue year of the first, second, third, and fourth national seismic zonation map in China, which defines the seismic design code for new buildings built afterwards. d – f The median fragility curves derived by Xin et al. 58 for brick-wood, mixed masonry, and steel-RC building types, respectively.

a – c Digitalized second, third, and fourth probabilistic seismic hazard maps issued in 1977, 1990, and 2001 by the China Earthquake Administration for the case study area (PGA: peak ground acceleration). d – f The vulnerability curves used for brick-wood and other, mixed masonry, and steel-RC with different seismic design code levels.

a – d The losses are calculated based on the rectified PGA maps in Fig. 4a–d using the PBS method. e – h The losses are calculated based on the rectified PGA maps in Fig. 4e–h using four empirical GMPEs (BA08, CB08, BSSA14, and CB14). Note the legend in Fig. 8 is the same as that in Fig. 5 .

The histograms in colour are based on seismic loss maps in Fig. 8a–h , while the histograms without fill colour are based on the empirical loss ratio function in Eq. ( 1 ) (Note: for BA08, CB08, BSSA14, and CB14, the maximum intensity is IX, while for PBS-based Scenarios 1-4, the maximum intensity is XI).

a The distribution of the intersected top 10% loss grids (in green) by combining the PBS-based seismic loss maps in Fig. 8a–d . b The distribution of the intersected top 10% loss grids (in green) by combining the GMPE-based seismic loss maps in Fig. 8e–h . c The distribution of intersected grids (in blue) by combining the top 10% loss grids in each seismic loss distribution map in Fig. 8 . Note: Intersected grids located on the boundary of two or three neighbouring counties/districts are counted repeatedly in the legend.

After obtaining the three layers of information required for risk modelling, namely the predictions of ground motion in different scenario earthquakes, the asset estimations for residential buildings in potential earthquake-affected areas, and the assignment of vulnerability curves for different building types (see the Methods section for details), the seismic loss can be estimated instantly for each earthquake scenario by convolving the ground motion map with the exposure and vulnerability model. Notably, the simulated PGA values (Fig. 2 ) are presented at 200 m × 200 m resolution, while the exposure values (Fig. 5 ) are at 1 km ×1 km resolution. Therefore, before the calculation of seismic loss, the PGA values are first resampled to 1 km × 1 km resolution, then rectified by the soil amplification factors (Table 1 ). The rectified PBS-based and GMPE-based PGA distribution maps for 1 km × 1 km sites with exposed residential buildings are shown in Fig. 4 . The final seismic loss distribution maps for the four earthquake scenarios with different nucleation positions in the Jiaocheng Fault with PGAs simulated by the PBS method in Zhang et al. 45 and by the four empirical GMPEs of “BA08” 46 , “CB08” 47 , “BSSA14” 48 and “CB14” 49 are shown in Fig. 8a–d and Fig. 8e–h , respectively (see the selection criteria of these GMPEs in the Methods section). The legend used in these loss distribution maps is the same as that used for the exposure distribution map in Fig. 5 .

From Fig. 8 it can be seen that in general, for regions far from the Jiaocheng fault, their seismic loss distribution pattern (namely the spatial locations of high and low loss grids) is quite different from the exposure distribution pattern (namely the locations of high and low exposure grids) in Fig. 5 , while for regions close to the Jiaocheng fault, the locations of high seismic loss grids in Fig. 8 resemble those of the high exposure grids in Fig. 5 . When calculated based on PBS-simulated PGAs, the overall seismic loss in panels (a–d) of Fig. 8 is 103.4 billion RMB, 103.3 billion RMB, 88.9 billion RMB, and 62.3 billion RMB, which accounts for 11.3%, 11.3%, 9.8%, and 6.8% of the total replacement value of residential buildings in the case study area (in total 911 billion RMB), respectively. Grids with losses higher than 100 million RMB are mainly centred around Taiyuan city due to its close distance to the Jiaocheng fault and high concentration of residential buildings. This characteristic does not change with the northeast shift of the epicentre of the earthquake scenario from panel (a) to panel (d) in Fig. 2 . When calculated based on GMPE-simulated PGAs, the total seismic loss in panels (e)-(h) of Fig. 8 is 176.3 billion RMB, 160.1 billion RMB, 171.2 billion RMB, and 175.4 billion RMB, which correspondingly accounts for 19.4%, 17.6%, 18.8%, and 19.3% of the replacement value of all exposed residential buildings in the case study area. Compared with the PBS-based loss distribution maps, the seismic losses estimated from GMPE-based PGA maps are generally higher, although the spatial distribution pattern of grid losses in all the panels in Fig. 8 is similar.

To better demonstrate the difference among different loss distribution maps in Fig. 8 , the rectified PGA maps (Fig. 4 ) are converted to seismic intensity by using the PGA-intensity conversion relation provided in the 2008 version of the China Seismic Intensity scale 50 . The maximum intensity converted from rectified PGAs in Fig. 4a–d (PBS-based) and Fig. 4e–h (GMPE-based) is XI and IX, respectively. The distribution of residential building replacement value within each intensity range is given in Fig. 11a . Figure 11b summarizes the corresponding seismic loss within each intensity range, which reveals that the seismic losses of the PBS-based and GMPE-based hazard maps are mainly from intensities VIII and IX, respectively. This finding indicates that when the hazard map is simulated by using the PBS method, the largest seismic loss may not necessarily originate from the maximum intensity zone, while when the hazard map is generated by empirical GMPEs, the highest loss tends to cluster in the maximum intensity zone. It will be easier to understand this result when combing the exposure distribution in Fig. 11a . For PBS-based Scenario 1 ~ 3, there are many more buildings located in VIII than in IX, X and IX (the detailed building replacement values are listed in Table 2 ); while for GMPE-based BA08, BSSA14 and CB14, the largest portion of exposure is in IX. In this case, the cluster of exposure data plays an important role in determining the concentration of loss in which intensity range. Based on the exposure and loss values in each intensity range in Fig. 11a , b (values are listed in Table 2 ), we further calculate the corresponding loss ratio, as shown in Fig. 11c . The loss ratio for the same intensity range turns out to be similar, although the intensity zone in the four PBS-based and four GMPE-based PGA maps may cover different grids within the case study area. This consistency indicates that such intensity-loss ratio pairs could be used to rapidly predict the overall seismic loss after the occurrence of a damaging earthquake, as the loss ratio in each intensity is stable regardless of whether the intensity map is generated from the GMPE or from the PBS method.

a The replacement value, ( b ) the seismic loss, and ( c ) the ratio between the seismic loss and replacement value of exposed residential buildings within each intensity range (Note: for BA08, CB08, BSSA14, and CB14, the maximum intensity is IX, while for PBS-based Scenarios 1-4, the maximum intensity is XI).

Delineation of the Regional High Seismic Risk Zone

It is specifically emphasized in the 2018-2022 strategic plan of the Federal Emergency Management Agency (FEMA) that the most successful way to achieve disaster resiliency is through preparedness 7 . In future earthquake disaster mitigation work, to reduce the potential number of casualties and economic losses more effectively, the top priority should be given to areas with high seismic risk considering the limited budgetary resources. Since the development of a seismic risk model is based on the modelling of seismic hazard, exposure, and vulnerability, uncertainty exists at every step of the seismic risk modelling chain. To determine those regional high seismic risk zones from such an uncertain process, the seismic risk modelling results from different earthquake scenarios and model inputs should be comprehensively combined.

Here, we propose a strategy to delineate the regional high seismic risk zone by combining the loss distribution maps in Fig. 8 . Since the seismic losses in Fig. 8a–d and Fig. 8e–h are calculated from PGAs simulated using different methods, we try to combine the PBS-based and GMPE-based seismic loss distribution maps separately and check whether their intersected high loss grids are similar. First, we line up the grids in each panel of Fig. 8 (there are 28808 1 km×1 km grids within each loss distribution map) from highest to lowest according to the loss in each grid. Then for each loss distribution map, we select the top 10% of grids with the highest relative losses (in total, 2881 grids) and generate the intersected high-loss grids separately from Fig. 8a–d and Fig. 8e–h . As shown in Fig. 10a and Fig. 10b , their intersected high seismic loss grids have different spatial distribution characteristics. The former is in a quasi-linear shape with 2266 1 km×1 km grids, while the latter is in an ellipse shape with 2667 1 km×1 km grids (the corresponding loss values are summarized in Table 3 ). In addition, the summed seismic loss of grids in Fig. 10a ranges from 53 billion to 93 billion, which accounts for 86% ~ 90% of the overall seismic loss in the corresponding PBS-based seismic loss map in Fig. 8a–d , while the summed seismic loss of the intersected grids in Fig. 10b ranges from 146 billion to 164 billion and accounts for 92% ~ 93% of the overall seismic loss of the corresponding GMPE-based seismic loss map in Fig. 8e–h . Therefore, the difference in the distribution of intersected high-loss grids between PBS- and GMPE-based PGA maps does exist. Since both PBS- and GMPE-based PGA maps have corresponding advantages and disadvantages, a more appropriate practice is to further combine their intersected high-loss grids to obtain the final regional high seismic risk zone, as shown in Fig. 10c .

There are 2007 grids (see Table 3 ) remaining in Fig. 10c , which accounts for 7% of the total number of grids (28808) exposed to potential seismic hazards in the case study area. These final intersected grids account for 83% ~ 88% of the overall seismic loss in corresponding panel in Fig. 8 . More importantly, their locations do not change with the variation in seismic hazard simulation methods or the shift in nucleation location of the scenario earthquakes. Therefore, the 1 km × 1 km grids in Fig. 10c are the regional high seismic risk zone delineated for the case study area with better confidence. With such information available, local policy makers can target their seismic risk mitigation measures in these grids, which is expected to be a more cost-effective approach. It is noteworthy that for practical reasons, earthquake risk mitigation programs may directly target at specific building types (i.e., unreinforced brick masonry or structures built several decades ago) 51 . For the scenario earthquakes considered in this paper, when we further categorize the economic losses in Fig. 8 by building types (brick-wood, steel-RC, mixed, other), as shown in Fig. 12a , it turns out that mixed masonry buildings (mainly refer to buildings with steams made of steel-concrete and the load-bearing walls made of brick-concrete) account for the largest share of overall seismic loss. Supposing all buildings are retrofitted to the high code level (in which case only the high code vulnerability curves shown in Fig. 7d–f are used for loss calculation), the corresponding losses after retrofitting and the decreased losses of each building type are shown in Fig. 12b and Fig. 12c , respectively. Figure 12b indicates that if all buildings are retrofitted to the high code level, for PBS-based scenarios the largest loss share is from the brick-wood buildings, while for GMPE-based scenarios, the largest loss share is still from the mixed masonry buildings. The largest decrease in seismic loss after retrofitting is from the mixed masonry buildings as well. However, these characteristics are gained when considering the overall loss of each building type in the case study area. When the loss distribution is checked in each 1 km × 1 km grid, the building type that dominates the loss may vary from grid to grid. This also indicates the necessity for mitigation programs at the local level to consider different types of buildings in different places. Specifically for those 2007 delineated high loss girds, such information needs to be investigated in detail before taking actions for engineering fortification.

a The distribution of seismic losses in Fig. 8 for each building type. b The losses calculated by assuming all buildings are retrofitted to the high code level, namely only the high code vulnerability curves shown in Fig. 7d–f are used for loss calculation. c The decreased loss after retrofitting.

For local policy makers, seismic risk mitigation resources are usually allocated based on administrative units. Therefore, we further combine the administrative information with the number of intersected grids in Fig. 10c . These grids belong to 30 different counties and districts, as listed in Table 4 . We suggest that the financial resources for seismic risk mitigation practice can be deployed based on the number of high seismic risk grids in the related county/district. However, it is worth emphasizing that the exposure model used in this paper only considers residential buildings. Before applying the delineated high-loss grids to the risk mitigation work for Taiyuan and its neighbouring area, the results in Fig. 10 and Table 4 should be modified by comprehensively considering the losses to both residential and non-residential buildings, as well as infrastructures 52 , 53 , 54 . Specifically, there are 240 high loss grids in Taiyuan; for around 33 of them, there are detailed building investigation results, as shown in Fig. 6a . Therefore, it is worthwhile to have a closer look at the structure types that dominate the risk in these grids in a future follow-on study by considering several different risk indicators (i.e., economic losses of all types of buildings, human fatalities, the number of collapsed buildings, etc.).

To establish the best model to delineate regional high seismic risk zone, the ground motion distribution maps for four earthquake scenarios are simulated by using both the PBS and GMPE methods and rectified by considering the soil amplification effect. The exposure model of residential buildings in the case study area is revised by considering the actual construction prices of buildings in this region, and their vulnerability curves are separately determined by comprehensively considering attributes such as the construction year, seismic code level, structure type and story class. Finally, the estimated seismic losses of both ground motion models for four earthquake scenarios at the Jiaocheng Fault are combined to delineate the high seismic risk zone in this region, which accounts for only ~7% of the regional land area but ~85% of the overall potential seismic loss. It is noteworthy that using a lethality lens versus an economic loss lens is likely to change the specific local areas prioritized for mitigation investment, because highly lethal residential buildings (i.e., older pre-code buildings) may not be the same as high-economic-loss residential buildings (i.e., larger or newer buildings). Further, deaths are most often related to collapse, while a building can be a complete economic loss without collapse. For example, the buildings in the Christchurch central business district were completely damaged and had to be demolished after the 2011 earthquake, but they seldomly collapsed 55 . This difference will be magnified once other building types (especially commercial) are considered. If seismic loss is considered the only criterion when assigning the limited budget resources, our findings indicate that the top priority should be given to those ~7% high loss grids when conducting seismic risk mitigation actions. However, since the earthquake scenarios considered in this paper are identified only as potential future scenarios to occur based on the available information, we have no corresponding recorded data to test the reasonability of the seismic loss estimation results. As an alternative, we turn to comparing our loss estimations with those calculated from the empirical loss function.

The empirical loss function used in this study is regressed from the intensity maps and damage information for historical earthquakes that occurred in the case study area and its neighbouring regions. These data were compiled in our previous work 56 , in which a composite catalogue of damaging earthquakes that occurred in mainland China (hereafter referred to as MCCDE-CAT) during 1950–2019 was established. In MCCDE-CAT, data including (a) the macroseismic intensity maps of historical earthquakes, (b) the population exposed to each intensity zone of each damaging earthquake, and (c) the recorded losses of damaging earthquakes have been compiled systematically. Based on the information in MCCDE-CAT and the estimated fixed capital stock value exposed to each damaging earthquake, we further develop a set of empirical economic loss estimation models for five different subregions in Mainland China (including the Xinjiang region, the Qinghai-Tibet Plateau region, the Northeast region, the North China region, and the South China region) 57 . The functional form of the empirical loss ratio function developed for North China (which covers the case study area in this paper) is given as follows:

where \(\beta ={3.469\times 10}^{-7}{and\; \theta }=6.192\) . In Fig. 9 , two different types of loss calculations within each intensity range are shown: the analytical losses are derived from the loss distribution maps shown in Fig. 8 (based on the rectified PGA maps in Fig. 4 , the exposure model in Fig. 5 , and the vulnerability curves in Fig. 7d–f ); the empirical losses are calculated using the empirical loss ratio function (or empirical vulnerability function) in Eq. ( 1 ), the intensity map converted from PGA maps in Fig. 4 , and the exposure data in Fig. 5 . The comparison in Fig. 9 is based on the loss ratio since the sum of the exposure data at each intensity is the same for these two types of loss calculations. The detailed loss values in each intensity range are also listed in Table 2 .

As shown in Fig. 9 , for intensity ranges of VI and XI (note XI is only available for the PBS-based intensity map), the losses derived from Fig. 8 are close to those calculated from the empirical loss ratio function, while for intensity ranges of VII, VIII, IX, and X (note X is available only for the PBS-based intensity map), losses of the former are higher than those of the latter. The main reason lies in their difference in the way building vulnerability is considered. In Fig. 8 , the differences in building structure types and seismic code design levels are considered by using the corresponding vulnerability curves in Fig. 7d–f . When using the empirical loss ratio function in Eq. ( 1 ) to calculate the loss, the vulnerability difference among buildings is ignored, and buildings within each grid are taken as a whole to calculate the overall seismic loss. More specifically, the vulnerability curves used in the analytical loss estimation is based on experimental fragility analysis data collected by Xin et al. 58 for different types of buildings, while the loss ratio function in Eq. ( 1 ) is empirically regressed from the intensity maps and damage information of past earthquakes occurred in the case study area and its neighboring regions. Despite this difference, the overall seismic losses in the maps in Fig. 8 are approximately 1.32 ~ 1.50 times the corresponding losses calculated from the empirical loss ratio function (see Table 2 for detailed values). This narrow loss range can be regarded as a testimony of the reasonability of our estimated losses in Fig. 8 . Then, the regional high seismic risk zone delineated from these loss maps can be prioritized for the implementation of targeted risk mitigation measures.

Understanding the scale and extent of potential risk is the first step to taking any effective seismic risk mitigation action. Different from the widely adopted PSHA (probabilistic seismic hazard assessment) map, which regulates the seismic design code of newly built buildings by considering the severity and frequency of all possible sources and magnitude ranges of future earthquakes, the strategy proposed here serves as a complement to the PSHA method 2 to secure the safety of people and their property in established buildings. Specifically, the strategy can be applied to cases in which the local governments want to strengthen the seismic resilience of existing buildings in their jurisdiction; notably, the delineated regional high seismic risk zones can be used to effectively allocate resources, and priority can be given to renovating or replacing fragile buildings. Compared with probabilistic risk modelling results, risk assessment based on earthquake scenarios can provide a clearer understanding of the consequences of possible earthquakes, which can better encourage proactive risk preparedness and mitigation investments 7 . The delineation strategy for regional high seismic risk zones proposed in this paper can be applied to other countries and regions for more targeted risk mitigation practice, especially for cities near seismically active faults.

The regional high seismic risk zone delineation strategy can have a variety of users for various departments, agencies, and community officials. For example, it can provide guidance for policymakers to identify where and why risk-sensitive land use planning interventions are necessary 59 , emphasizing the need for proactive land use planning, zoning regulations, infrastructure improvements, and earthquake preparedness awareness to enhance community resilience 60 . Knowing the severity of seismic risk that threatens a city and the location of the most seriously affected zone also helps local policy makers prioritize seismic retrofitting of critical structures/infrastructures or identify areas where emergency response plans need to be developed, which can help prevent and reduce seismic hazard exposure and vulnerability, increase response and recovery preparedness, and enhance the resilience of the local community to the largest extent before earthquakes occur. Emergency response teams may focus on the delineated high seismic risk zone to plan and perform emergency response exercises. Emergency planners can also comprehensively determine the temporary shelter requirements for different earthquake scenario events 61 . In addition, further investigation on the financial sustainability of seismic risk reduction programs can also be conducted focusing on specific building types that dominate the seismic loss 62 , thus to better plan mitigation measures and improve the level of preparedness in case of an earthquake, particularly in urban areas where human activities are concentrated 63 . Only with continuous investment in these pre-disaster risk mitigation efforts can we truly protect life and property, reduce the increasing cost of earthquake disasters, and secure better sustainable development of society. We believe that the proposed strategy will add value to current knowledge for disaster risk management, providing a clearer reference for local risk managers to conduct risk mitigation actions.

The limitations of this study are multifaceted. First, although we employ two widely used methods (namely the physics-based method and the empirical GMPE method) to simulate ground shaking in earthquake scenarios, only the PGA values are used for building vulnerability assessments, and spectral accelerations with different periods are not considered. This is limited by the reliable frequency range of ground motions computed by the PBS method 45 , which is relatively low (typically up to approximately 1.5 Hz) due to restrictions in computing capacity and the lack of a high-resolution medium velocity model. Second, the site amplification effect is only empirically rectified based on the site classification results of Li et al. 44 ; currently, this is the best dataset we could find to perform such rectification for our case study area. Third, the replacement value of residential buildings is given at a 1 km × 1 km resolution and not estimated building by building. This is limited by the availability of investigation data for individual buildings for the whole case study area. Fourth, to demonstrate our strategy, the earthquake scenarios considered are only those with a magnitude of approximately Mw 7.5, and the exposure model only includes the residential buildings in the study area; thus, the delineated high seismic risk zone in this paper cannot be applied directly into seismic risk mitigation practice before being revised by considering more magnitude ranges and building types. Fifth, earthquake scenarios are assumed and have not yet occurred in the Jiaocheng Fault. Therefore, it is not possible to test the seismic loss estimations using observation records. Furthermore, we are also fully aware that complete risk assessment requires a holistic analysis of different types of losses, including social (fatalities and injuries) and financial losses (direct losses to all exposed fixed assets and indirect losses caused by business interruption, etc.). Here, we choose the financial losses of residential buildings to differentiate the seismic risk level. Other seismic risk indicators (e.g., collapsed buildings, human fatalities etc.) can also be explored in a similar way, but the calibration process of exposure and vulnerability information will be different 2 , 64 , which might delineate different risk priority zone. However, an in-depth discussion on the use of different risk indicators is quite beyond the focus of this study. Additionally, the strategy proposed here only considers one fault. In regions threatened by multiple faults (such as areas near subduction zones facing both undersea and onshore faults or regions developed with multiple parallel faults), the number of scenarios considered should be increased to better include different rupture patterns on each of those faults. Further, the regional high seismic risk zone delineation strategy proposed in this paper is only the first step for actual seismic risk mitigation practice. More further studies (e.g., including evaluating the risk of building collapse and life loss) are needed to determine detailed measures and target safety levels for seismic intervention and timescales within which intervention must take place, which will be considered in our future work.

We emphasize that the focus of this paper is to propose a strategy that can identify a regional high seismic risk zone that is not specific to the simulation method of the hazard or the nucleation location of the earthquake. The final seismic losses also have reasonable consistency with empirical loss estimations. In addition, the seismic hazard, exposure, and vulnerability inputs used to derive the final losses have been calibrated and validated based on other hazard simulation methods 36 , exposure datasets 65 and vulnerability curves 58 . All these validation steps ensure the reasonability of our estimated losses and the accuracy of the delineated high seismic risk zone in the case study area. Therefore, the identified regional high seismic risk zone can be selected as the target of seismic risk mitigation efforts in Taiyuan and its neighbouring areas. In the future, to improve the accuracy of high seismic risk zone delineation, we will consider enlarging the frequency range of simulated ground motions by introducing artificial neural networks (ANNs) into our simulation 66 , and enriching earthquake scenario analysis by considering wider magnitude ranges and more nucleation positions. Additionally, exposure and vulnerability analyses for non-residential buildings and infrastructures can be combined with the proposed approach. It is noteworthy that changes in data inputs (e.g., spatial resolution, unit construction price, building classification typology, average floor area per building type etc.) for exposure and vulnerability modelling will doubtlessly alter the loss estimation results. Therefore, in the future it is quite necessary to further explore the effect of changes in these factors on the modelled exposure, estimated loss and the final delineated regional high seismic risk zone. Currently, the first nationwide natural disaster risk investigation work is ongoing in China, and detailed structural information for individual buildings in many sample areas is being collected. If such data could be publicly accessible in the future, we could further refine the exposure model and conduct vulnerability analyses of individual buildings. These improvements would support more comprehensive and reliable delineation of regional high seismic risk zones in the future.

In this paper, we use the earthquake scenarios set on the Jiaocheng fault in the Shanxi Rift System, North China, to demonstrate the delineation strategy of the regional high seismic risk zone. The Jiaocheng Fault lies on the northwest of the Taiyuan basin and mainly strikes in the southwest‒northeast (SW‒NE) direction and approaches the city of Taiyuan from the west within a small distance (Fig. 1 ). The extent of the earthquake-affected area considered in this paper ranges from 110.8° N to 114.24° N longitude and from 35.8° E to 38.5° E latitude. GPS observations 67 , 68 , 69 indicate that the Taiyuan Basin is in a horizontal extensional environment. Therefore, the Jiaocheng Fault was set as a normal fault with a dip angle of 60° 45 . The delineation of the regional high seismic risk zone is based on the combination of seismic loss distribution maps, which are calculated from ground shaking predictions, the replacement value of exposed buildings, and their corresponding vulnerability curves. In the following sections, we will separately introduce the generation process of these components.

Ground shaking simulated by the PBS method

To understand the seismic hazard caused by different earthquake scenarios on the Jiaocheng Fault, Zhang et al. 45 studied the dynamic rupture and propagation process of four earthquake scenarios that nucleated at different locations but with the same hypocentre depth by using the PBS method (as represented by the black stars in Fig. 1 ). The magnitude of these four scenarios was set to ~Mw 7.5 according to the tectonic background and historical seismicity related research findings on the Jiaocheng Fault 44 , 70 . The nucleation patches in these four scenarios were of the same size (with a radius of 2 km) and depth (8.66 km underground, as determined by trial-and-error test to achieve an ~Mw7.5 earthquake). Within the nucleation patch, a relatively high stress (0.1%) larger than the fault’s strength was imposed to initialize the dynamic modelling. As shown in Fig. 1 , the hypocentres of the second and fourth scenarios were in two corners divided by Wenshui and Qingxu counties, respectively. These two corners acted as barriers, which affected the rupture of the fault. The Scenario 1 and 3 earthquakes were triggered by the hypocentres located in the southern and middle segments of the Jiaocheng Fault, respectively. These four nucleation locations are considered quite representative and allow for the observation of the major difference in ground motions among scenario earthquakes on the Jiaocheng Fault. The further change of nucleation location will not present obviously different ground motion distribution patterns from those of the four scenario earthquakes considered in this study. This is because the purely physics-based dynamic simulations in Zhang et al. 45 are relatively stable and have the potential to reduce the ground motion variance at specific sites when compared with kinematic parameterization of the source, in which rupture velocity is specified without direct constraints from rupture dynamics 71 and more simulations are required to better characterize the ground motion variation.

Zhang et al. 45 generated peak ground accelerations (PGAs), peak ground velocities (PGVs) and spectral accelerations (SAs) in different periods by using the curved grid finite-difference method (CG-FDM), which was first proposed by Zhang et al. 72 to simulate seismic wave propagation in the presence of irregular topography and was further applied by Zhang et al. 73 to model the dynamic rupture of irregular planes. The validity of the CG-FDM was demonstrated in the recent benchmark exercises of Harris et al. 36 , which were designed to test whether different dynamic earthquake rupture algorithms can produce the same results given the same set of model assumptions. The dynamic rupture and propagation processes of the four earthquake scenarios at the Jiaocheng Fault, the distribution of the final slip patterns, and the ground shaking characteristics in terms of PGV were discussed in detail by Zhang et al. 45 . In this paper, we use their simulated PGA results in Fig. 2a–d with a grid interval of 250 m as the hazard inputs to assess the seismic risk associated with these four earthquake scenarios.

Ground shaking simulated by empirical GMPEs

Since ground shakings predicted by empirical GMPEs remain popular in current seismic hazard and risk assessment practice, we generate GMPE-based ground shaking maps for the four earthquake scenarios at the Jiaocheng Fault. The GMPEs used in this paper were developed by Boore and Atkinson (abbreviated as “BA08”) 46 , Campbell and Bozorgnia (abbreviated as “CB08”) 47 , Boore et al. (abbreviated as “BSSA14”) 48 , and Campbell and Bozorgnia (abbreviated as “CB14”) 49 . BA08 and CB08 are chosen since they have been tested and are suitable for the probabilistic seismic hazard assessment for the Shanxi Rift System as described by ref. 44 , which covers our case study area. The compatibility of PGV values generated with the PBS method by Zhang et al. 45 and based on the GMPEs of BA08 and CB08 were also verified in our prior work 37 . BSSA14 and CB14 are the updated versions of BA08 and CB08, respectively; thus, they are also used to generate the empirical ground shaking maps and calculate the subsequent seismic risks. A local GMPE specifically developed for the Shanxi Rift System that covers our case study area by Wen et al. 74 based on local intensity data of 19 Ms≥5.0 historical earthquakes was not used, since the ground motions predicted by this local GMPE have a significant deviation from other historical seismic data 44 . Other empirical GMPEs regressed from more sufficient recordings and specifically developed for the fifth national seismic hazard map in China by Yu et al. 75 are not chosen, since in these models the epicentral distance (R epi ) or hypocentral distance (R hyp ) is used, and the inherent assumption behind the use of R epi or R hyp is that the rupture source of an earthquake can be regarded as a point source. However, the earthquakes considered in this paper have a magnitude of ~Mw 7.5, and because of the complexity of the 3D geometry of the Jiaocheng Fault (Fig. 1b ), the point source assumption is not appropriate 37 .

The ground shaking maps generated with the four selected GMPEs are shown in Fig. 2e–h with the same grid interval of 250 m as that in Fig. 2a–d . In Fig. 2e and Fig. 2g , PGAs are calculated by considering only the magnitude scaling term and the distance function term in Eq. ( 1 ) of Boore and Atkinson for BA08 and BSSA14; in Fig. 2f , the magnitude term, the distance term and the style-of-faulting term in Eq. ( 1 ) of CB08 are considered, while the PGAs in Fig. 2h are calculated by considering more functional terms in Eq. ( 1 ) of CB14, such as the earthquake magnitude, geometric attenuation, style of faulting, hanging wall geometry, hypocentral depth, fault dip, and anelastic attenuation. All the PGA values in Fig. 2e–h are median predictions without considering the standard deviation term in the GMPEs. Additionally, a shift in the nucleation location of the earthquake scenarios at the Jiaocheng Fault will not change the final PGA distribution map predicted by using these four empirical GMPEs; therefore, in each panel of Fig. 2e–h , the PGA distribution map is the same for all four scenario earthquakes. In contrast, the PBS-based PGA map in Fig. 2a–d changes with the nucleation location, which results in improved spatial heterogeneity and a higher resolution of the ground motion distribution. The characteristics of and the differences between the PBS-based and GMPE-based PGA maps used in this paper are similar to those for PGV maps, which were analyzed in detail in our previous work 37 . In general, the attenuation trends of PGAs with source-to-site distances simulated with the PBS method display better consistency with those predicted based on CB08 and CB14 than those predicted based on BA08 and BA14. However, the use of different GMPEs in generating the PGA map will not obviously change the relative difference in PGA values of grids within each map 2 , thus the delineated regional high seismic risk zone will remain highly consistent.

Site effect rectification for simulated PGA distribution maps

Due to the lack of definite information on the plasticity of the Taiyuan basin and the local site conditions near the surface, Zhang et al. 45 only considered the elastic response of the propagation media in their dynamic rupture simulation. Thus, the PGA predictions shown in Fig. 2a–d have not yet been rectified based on site conditions. Correspondingly, the site terms of the four empirical GMPEs are also not considered when generating the median PGA maps in Fig. 2e–h . However, it has been frequently shown for previous earthquakes that local site conditions can significantly amplify seismic ground motion 76 , 77 , 78 . V S30 , the travel-time-averaged shear-wave velocity in the top 30 m of soil deposits, is typically used to represent the site conditions. The reference site conditions for BA08 and BSSA14 include V S30  = 760 m/s, which is classified as a Type B/C condition according to the site classification of the National Earthquake Hazards Reduction Program (NEHRP) 79 . For CB08 and CB14, the reference rock outcrop is characterized by V S30  = 1070 m/s (namely NEHRP Type B). The minimum shear velocity in the 3D media considered in the dynamic simulations of Zhang et al. 45 was V s  ≥ 1000 m/s.

Since detailed V S30 information is not publicly available for our case study area, to perform site effect rectification for the simulated PGAs in Fig. 2 , we use the site classes assigned by ref. 44 for the Shanxi Rift System (which covers our study area) to differentiate the site amplification effects. Based on the shear-wave velocity profiles in boreholes at 3106 engineering sites, 31 strong motion stations and 65 seismic stations in Shanxi Province and its neighbouring area, Li et al. 44 divided the whole Shanxi Rift System into five site classes according to the definitions (Table 5 ) in the fifth national Seismic Ground Motion Parameter Zonation Map issued in 2015 (GB 18306-2015) 80 for China. The site classification map used by Li et al. 44 is digitalized in Fig. 3a , based on which we generate the continuous site distribution map for our case study area shown in Fig. 3b . According to the site class definitions in Table 5 , the reference shear-wave velocity in the empirical GMPEs for BA08 and BSSA14, CB08 and CB14, and of Zhang et al. 45 corresponds to site classes I1, I0, and I0, respectively. Following the site adjustment factors given in GB 18306-2015, the PBS-based and GMPE-based PGAs in Fig. 2 are rectified to the site classes in Fig. 3b by using the corresponding adjustment coefficients defined in Table 1 . Notably, for certain regions in Fig. 3b (mainly in the southeast and northwest corners of the case study area), there is no available site classification information for reference; thus, we simply assume they are site class II (the base rock layer). The rectified PBS-based and GMPE-based PGA distribution maps for sites with exposed residential buildings are shown in Fig. 4 .

The exposure model for residential buildings

By using the downscaling method, Xin et al. 65 developed a publicly accessible residential building stock model (with 1 km × 1 km resolution) for three different levels (namely urban, township, and rural, as differentiated by population density) of 31 provincial administrative units in mainland China based on statistics from the 2010 population census of China and the population density profile released by the 2015 Global Human Settlement Layer project 81 . In this model, the floor areas and replacement values of 17 building subtypes (with different story classes and structure types; see Table 6 ) are given. However, for computational convenience, the unit construction price for each building subtype within each 1 km × 1 km grid was the same for the whole country in the study of Xin et al. 65 (see their Table 7). We are fully aware that the construction prices of building stocks in China significantly vary across the country due to the variations in economic development level, geographic and climatic diversity, and standardization in building construction. Thus, in this paper, the replacement values of the 17 building subtypes of Xin et al. 65 are modified specifically for Taiyuan and the neighboring area according to the construction prices issued by the Department of Housing and Urban-Rural Development of Shanxi Province (Table 6 ).

The distribution map of the modified residential building replacement value for Taiyuan and its neighbouring area, which reaches 911 billion RMB (in current price) in total, is shown in Fig. 5 . We also analysed the building floor area data at each urbanity level for the case study area. The floor area ratios of buildings with different structure types and story classes are plotted in Fig. 13a , b , respectively. In each panel, the floor area ratio refers to the floor area percentage of corresponding building type in each urbanity level (rural, township, urban). In terms of structure type, brick-wood and other low construction quality buildings (e.g., bamboo structure, brick arc structure, cave structure, etc.) tend to be dominant in rural areas, while mixed masonry and steel-RC buildings are more common in township and urban areas. In terms of the storey class, it is obvious that most rural buildings are only 1 storey or 2-3 stories, while most high storey buildings are in urban areas.

a The floor area ratio distribution classified by structure type in urban/township/rural levels of the case study area (BRIWO, STLRC, MIXED, and OTHER refer to brick-wood, steel-reinforced concrete, mixed masonry and other building types, respectively). b The floor area ratio distribution classified by storey classes (1, 2–3, 4–6, 7–9, ≥ 10) in urban/township/rural levels of the case study area. c Comparison of the modelled floor area of residential buildings in Xin et al. 65 with records in the 2010 census of China at the county level for the case study area.

To further verify the general reliability of the residential building exposure model in Fig. 5 , we compared the modelled floor area with census records at the county level, as shown in Fig. 13c . The R 2 value of these two sets of data reaches 0.97, which is quite high. It is also noteworthy that the modelled floor area is generally higher than that recorded in the census, since during the modelling process in Xin et al. 65 , they scaled the building-related census records from 2010 to 2015 by multiplying the population amplification ratio from 2010 to 2015 for each urbanity level, which is approximately 1.07, 1.16 and 1.19 for rural, township, and urban areas in Shanxi Province, respectively.

Vulnerability curves for different building types

Ideally, the vulnerability curves for different building types should be determined by considering the structure type, the number of stories, the built age, the seismic design code and the variation in seismic design code with time. The residential building stock model in Xin et al. 65 has not provided sufficient information on structure characteristics, in which only the number of stories and structure type attributes are available, but other key attributes, such as the year the structure was built and its seismic design code, are missing. Therefore, in this paper, we devote more effort to investigating built year and seismic code information for residential buildings within the case study area. Experts from the China Earthquake Administration conducted a building-by-building survey in the downtown area of Taiyuan city, in which the address, construction year, structure type, footprint extent and floor area of individual buildings were recorded, as shown in Fig. 6a . It is noteworthy that these surveyed buildings take up around 33 1 km × 1 km grids in spatial extent and they are not used to construct the exposure model in Fig. 5 . In this survey, 63712 buildings were investigated, but 10973 buildings were demolished during the investigation. Thus, we analyse the remaining 52739 buildings to determine the dominant construction period of buildings with different story classes and structure types.

In Fig. 6b, c , the investigated construction year information is regrouped into several periods, namely 1957–1976, 1977–1989, 1990–2000, and 2001–2014. These four periods are grouped according to the issue year of the first, second, third, fourth, and fifth national seismic zonation maps in China, which is 1957, 1977, 1990, 2001, and 2015, respectively. Each seismic zonation map regulates the seismic design code of new buildings built after its issue year until the appearance of an updated map. Based on the ratio of buildings (namely the percentage of all 52739 surveyed buildings) classified by the combination of construction period and story class/structure type in panel (b, c) of Fig. 6 , we can approximately determine the dominant construction period for buildings with different combinations of story class and structure type. For example, the construction period for a 1-storey brick-wood building is most likely to be within the range of 1977-1989. As summarized in Table 7 , the construction periods for the investigated buildings in the downtown area of Taiyuan are mainly in the ranges of 1977-1989, 1990-2000, and 2001–2014. To differentiate the vulnerability of buildings built in accordance with different versions of seismic design codes, we digitalized the second, third and fourth zonation maps issued in 1977, 1990 and 2001 by the China Earthquake Administration for our case study area, as shown in Fig. 7a–c . Combining the dominant construction period with seismic design code information, the vulnerability levels (precode, low-code, moderate-code, high-code) of different building types in the case study area can be determined (as listed in Table 8 ) according to the judgement criteria modified after Table 8 in Lin et al. 82 .

The building vulnerability curve (which describes the relationship between the ground shaking indicator and loss ratio; loss ratio refers to the ratio between the structure repairment cost and structure replacement cost) is generated by combining the building fragility curve and the consequence function. A fragility curve describes the probability of a building being in different damage states when undergoing different ground shaking levels. Xin et al. 58 performed a review of fragility curves for major building types in mainland China based on empirical post-earthquake survey data and analytical structural response experiment data, in which different ground shaking indicators (macro-seismic intensity for the former and PGA for the latter) were used. The median fragility curves derived for brick-wood, mixed masonry and steel-RC by Xin et al. 58 based on analytical experimental data will be used in this paper, as plotted in Fig. 6d–f . Combining the building fragility curve with the corresponding consequence function, as listed in Table 9 for brick-wood 83 and in Table 10 for mixed masonry and steel-RC 84 , the vulnerability curves for these building types with different seismic design code levels can be determined, as shown in Fig. 7d–f . For each building type, the vulnerability curves for three different seismic design code levels (pre/low-code, moderate-code, and high-code) are empirically assigned by using the upper, mean, and lower thresholds of the loss ratio given in the corresponding consequence function. For “other” building types (e.g., bamboo structure, brick arc structure, cave structure, etc.) in Table 7 , there is no more detailed information on its exact building type; thus, we assume that storey classes 1 and 2-3 have the same vulnerability curves as the “brick-wood” building in Fig. 7d , while storey classes 4–6, 7–9, and ≥10 have the same vulnerability curves as the “mixed” masonry building in Fig. 7e . It is noteworthy that in locations where detailed building-by-building inventories are not available, researchers can still use the regional high seismic risk zone delineation strategy proposed in this study if they can generate reasonable estimates of exposure on a 1 km x 1 km grid (or a lower resolution) and have fragility or vulnerability functions for the main building types.

Data availability

The ground motion prediction results shown in this study can be shared upon request. The exposure data (namely the replacement value of residential buildings) are available from https://doi.org/10.5281/zenodo.4669800 . The fragility data used for vulnerability curve development are available from https://nhess.copernicus.org/articles/20/643/2020/ .

Code availability

The codes used for this study can be shared upon request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Number 42204054, 42174057) and the Shenzhen Stable Support Plan Program for Higher Education Institutions (Grant Number 20220815084720001). We appreciate the constructive comments from Prof. John Douglas and three anonymous reviewers, which have greately improved the quality of this paper. The authors also want to express their gratitude to Prof. Lingling Ye and Prof. Shiqing Xu for their suggestions during the paper preparation process.

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Danhua Xin, Zhenguo Zhang, Yilong Li & Xiaofei Chen

Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, 518055, China

Danhua Xin, Zhenguo Zhang & Xiaofei Chen

Institute of Geophysics, China Earthquake Administration, Beijing, 100081, China

Geophysical Institute, Karlsruhe Institute of Technology, Hertzstraße 16, 76187, Karlsruhe, Germany

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D.X. designed the approach, performed the analysis, and prepared the draft manuscript. Z.Z. guided the project and provided the physics-based ground motion simulation results. B.C. provided the investigation data for individual buildings in the downtown area of Taiyuan. F.W., Y.L. and X.C. provided in-depth discussion. All authors contributed to the revision of the manuscript.

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Correspondence to Zhenguo Zhang .

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Xin, D., Zhang, Z., Chen, B. et al. Can we develop a more targeted approach to mitigating seismic risk?. npj Nat. Hazards 1 , 19 (2024). https://doi.org/10.1038/s44304-024-00020-z

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DOI : https://doi.org/10.1038/s44304-024-00020-z

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