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Several natural hazards, including earthquakes, may trigger disasters and the presence of disaster drivers further lead to the massive loss of life and property, every year around the world. The earthquakes are unavoidable, as exact earthquake prediction in terms of date, and time is difficult. However, with the advancement in technology, earthquake early warning (EEW) has emerged as a life-saving guard in many earthquake-prone countries. Unlike other warning systems (where hours of warning are possible), only a few seconds of warning is possible in the EEW system, but this warning may be very helpful in saving human lives by taking the proper action. The concept of EEW relies on using the initial few seconds of information from nearby instruments, performing basic calculations, and issuing the warning to the farther areas. A dense network or enough network coverage is the backbone of an EEW system. Because of insufficient station coverage, the estimated earthquake location is error-prone, which in turn may cause problems for EEW in terms of estimating strong shaking for the affected areas. Seismic instrumentation for EEW has improved significantly in the last few years considering the station coverage, data quality, and related applications. Many countries including the USA, Mexico, Japan, Taiwan, and South Korea have developed EEW systems and are issuing a warning to the public and authorities. Several other countries, namely China, Turkey, Italy, and India are in process of developing and testing the EEW system. This article discusses the challenges and future EEW systems developed around the world along with different parameters used for EEW.Article HighlightsThis article aims to provide a comprehensive review related to the developmentThe explicit emphasis is on the scientific development of EEW parametersThe challenges and future scopes for the effective implementation of EEWS are discussed in terms of the correct location, the magnitude estimation, the region-specific use of ground motion prediction equations, communication technologies, and general public awareness

Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

Estimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield‐based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake. Plain Language Summary Have you ever heard something through the grapevine? It often takes you by surprise to hear a message from someone other than the original source. You might have felt an earthquake in a similar way: experiencing shaking (the message) at your location rather than movement along a fault (the source). We apply grapevine‐style communication to earthquake early warning (EEW). The goal of EEW is to warn people to prepare for earthquake shaking before damaging seismic waves arrive at their location. We build on recent work that used deep learning and large earthquake data sets to predict earthquake shaking. We developed a deep learning algorithm called GRAPES which predicts shaking in a manner similar to a game of seismic telephone: when a seismic sensor detects shaking, it sends a message to its neighboring sensors, warning them to expect shaking soon. These sensors then pass on the message to their more distant neighbors along the grapevine. We show that the messages GRAPES learned to send between sensors are like seismic status updates: “I'm seeing this type of seismic wave right now”. We applied GRAPES to the 2019 M7.1 Ridgecrest, CA earthquake and it predicted shaking accurately and quickly. Key Points A deep learning network trained to predict ground motion learned an internal representation of the seismic wavefield Individual neurons within the network activate with the arrival of P waves, S waves, surface waves, coda waves, and ambient noise While trained on earthquakes in Japan, the model generalizes well to predicting ground motions for the 2019 Ridgecrest, CA earthquake

An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT efforts for EEWS. Fifth, it proposes a generic EEWS architecture based on IoT and ML. Finally, the paper addresses the application of ML for earthquake parameters’ observations leading to an efficient EEWS.

The MyShake Platform is an operational framework to provide earthquake early warning (EEW) to people in earthquake-prone regions. It is unique among approaches to EEW as it is built on existing smartphone technology to both detect earthquakes and issue warnings. It therefore has the potential to provide EEW wherever there are smartphones, and there are now smartphones wherever there are people. The MyShake framework can also integrate other sources of alerts and deliver them to users, as well and delivering its alerts through other channels as needed. The MyShake Platform builds on experience from the first 3 years of MyShake operation. Over 300,000 people around the globe have downloaded the MyShake app and participated in this citizen science project to detect earthquakes and provide seismic waveforms for research. These operations have shown that earthquakes can be detected, located, and the magnitude estimated ~ 5 to 7 s after the origin time, and alerts can be delivered to smartphones in ~ 1 to 5 s. A human-centered design process produced key insights to the needs of users that have been incorporated into MyShake2.0 which is being release for Android and iOS devices in June 2019. MyShake2.0 will also deliver EEW alerts, initially in California and hopes to expand service to other regions.

Rapid and accurate prediction of peak ground acceleration (PGA) is an important basis for determining seismic damage through on-site earthquake early warning (EEW). The current on-site EEW uses the feature parameters of the first arrival P-wave to predict PGA, but the selection of these feature parameters is limited by human experience, which limits the accuracy and timeliness of predicting peak ground acceleration (PGA). Therefore, an end-to-end deep learning model is proposed for predicting PGA (DLPGA) based on convolutional neural networks (CNNs). In DLPGA, the vertical initial arrival 3–6 s seismic wave from a single station is used as input, and PGA is used as output. Features are automatically extracted through a multilayer CNN to achieve rapid PGA prediction. The DLPGA is trained, verified, and tested using Japanese seismic records. It is shown that compared to the widely used peak displacement (Pd) method, the correlation coefficient of DLPGA for predicting PGA has increased by 12–23%, the standard deviation of error has decreased by 22–25%, and the error mean has decreased by 6.92–19.66% with the initial 3–6 s seismic waves. In particular, the accuracy of DLPGA for predicting PGA with the initial 3 s seismic wave is better than that of Pd for predicting PGA with the initial 6 s seismic wave. In addition, using the generalization test of Chilean seismic records, it is found that DLPGA has better generalization ability than Pd, and the accuracy of distinguishing ground motion destructiveness is improved by 35–150%. These results confirm that DLPGA has significant accuracy and timeliness advantages over artificially defined feature parameters in predicting PGA, which can greatly improve the effect of on-site EEW in judging the destructiveness of ground motion.

The Noto Peninsula earthquake (Mj7.6), which occurred on New Year’s Day of 2024, had two characteristic features: multiple tremors at the initiation of the rupture and a long fault rupture exceeding 100 km. The source process included three significant tremors for 15 s: Mj ~ 3 event, Mj 5.9 event, and Mj 7.6 event. The rupture started at the tip of the Noto Peninsula and propagated bilaterally in northeast and southwest directions. We evaluated the performance of the Japanese earthquake early warning (EEW) issued to the public. The source determination process of the EEW was triggered by the preceding Mj ~ 3 event and the warning threshold was exceeded by the Mj 5.9 event, so there was at least a 13-s lead time before the S-arrival of the Mj 7.6 event, allowing many residents to take protective measures. The first warning was issued to only the Northern part of the Ishikawa prefecture. However, the second warning that was distributed to as far as a few hundred kilometers was issued 27.1 s after the first warning, which was longer than expected. This is because the magnitude was underestimated during the rupture process and the warning was issued based on the shaking observation of the Mj7.6 event. We recomputed the shaking estimation from the Integrated Particle Filter (IPF) method and the Propagation of Local Undamped Motion (PLUM) method used in the Japanese EEW, and additionally, the XYtracker method to evaluate the effect of fault finiteness. At the initial part of the rupture, the fault finiteness is difficult to capture, and the finite-source approach produced a similar shaking estimation to the point-source approach. As the rupture propagates, shakings in the western area near the fault were significantly underestimated by the point-source approach. For large earthquakes, considering fault finiteness may be able to capture the rupture directivity and improve the accuracy of shaking estimation. Graphical Abstract

Rapid and accurate earthquake magnitude estimations are essential for earthquake early warning (EEW) systems. The distance information between the seismometers and the earthquake hypocenter can be important to the magnitude estimation. We designed a deep-learning, multiple-seismometer-based magnitude estimation method using three heterogeneous multimodalities: three-component acceleration seismograms, differential P-arrivals, and differential seismometer locations, with a specific transformer architecture to introduce the implicit distance information. Using a data-augmentation strategy, we trained and selected the model using 5365 and 728 earthquakes. To evaluate the magnitude estimation performance, we use the root mean square error (RMSE), mean absolute error (MAE), and standard deviation error ( ϭ ) between the catalog and the predicted magnitude using the 2051 earthquakes. The model could achieve RMSE, MAE, and ϭ less than 0.38, 0.29, and 0.38 when the passing time of the earliest P-arrival is 3 s and stabilize to the final values of 0.20, 0.15, and 0.20 after 14 s. The comparison between the proposed model and model ii, which is retrained without the specific architecture, indicates that the architecture contributes to the magnitude estimation. The P-arrivals picking error testing indicates the model could provide robust magnitude estimation on EEW with an absolute error of less than 0.2 s. Graphical Abstract

As one of the most earthquake-prone regions in the world, China faces extremely serious earthquake threats, especially for those heavily populated urban areas located near large fault zones. To improve the ability to prevent and minimize earthquake disaster risks, and to reduce earthquake disaster losses, China is currently building a nationwide earthquake early warning system (EEWS) with the largest seismic network in the world. In this paper, we present the newest progress of this project through describing the overall architecture of the national EEWS and evaluating the system performance during the 2022 Lushan M6.1 earthquake. The accuracy of the source characterization for the Lushan earthquake is discussed by comparing the continually estimated location and magnitude with the catalogs obtained from the China Earthquake Networks Center. For this earthquake, the EEWS generated a total of five alerts, and an initial alert was created 5.7 s after its occurrence, with excellent epicentral location and origin time estimation. The final alert was issued 16.5 s after origin time with a magnitude estimate of M6.1, the same as the catalog value. However, from the point view of alerting performance, the radius of the real blind zone without warning time was about 30 km and much larger than the theoretical result, mainly caused by the releasing system not considering the epicenter distance of each terminal when issuing the alerts. Although the earthquake exposed some limitations that need to be addressed in future upgrades, the results showed that most aspects of the EEWS presented a robust performance, with continuous, reliable event detections and early-warning information releasing.

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.