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An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named "INSTANCE," comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs). [ABSTRACT FROM AUTHOR]

The editorial discusses the advancements in earthquake hazard and risk communication, emphasizing the importance of innovative educational approaches and multidisciplinary research to enhance societies' resilience towards earthquakes. It highlights the societal impact of earthquake early warning systems and the need for public education, trust, and clear communication strategies to ensure effective responses to alerts. The editorial also explores the use of escape rooms as an educational tool to enhance seismic awareness among young people, emphasizing the importance of interactive learning methods in disaster preparedness. [Extracted from the article]

Earthquake early warning systems have become vital for minimizing damage from seismic events. However, their automated detection capabilities need strengthening to provide real-time alerts. Current algorithms have limitations in identification of P-waves and magnitude estimation, impacting warning lead times. Additionally, existing single-algorithm dependent systems are prone to errors. There is a need for standardized practices to optimally select and combine algorithms. Machine learning and artificial intelligence show promise to make detection more robust. Models trained on diverse seismological data can learn complex patterns to detect emergent P-waves earlier and refine magnitude assessment. However, research exploring such data driven approaches within early warning systems is limited. This study aims to address this research gap and strengthen automated detection capabilities. It proposes a machine learning model integrating multiple existing algorithms using a novel prioritization framework. Performance is evaluated on real earthquake datasets through simulations vis-à-vis single algorithms. By developing an optimized multi-algorithm framework, this study seeks to improve warning lead times and reliability. The model is designed considering operational requirements of early warning systems. Comparison of results with past methods helps evaluate contributions to the field. Overall, the research strives to enhance seismic hazard mitigation through more efficient automated detection in early warning networks. [ABSTRACT FROM AUTHOR]

Can a P-wave detection algorithm enhance the performance of an Earthquake Early Warning System (EEWS), particularly in community-engaged networks of low-cost ground motion sensors susceptible to noise? If so, what P-wave detection algorithm would perform the best? This study analyses the performance of four different P-wave detection algorithms using a community-engaged Earthquake Early Warning (EEW) network. The ground motion data from a 48-hour time window around a M5.8 earthquake on 22 September 2022 were used as the basis for this case study, where false and missed detections were analysed for each P-wave detection algorithm. The results indicate that a wavelet transformation-based P-wave picker is the most suitable algorithm for detecting an earthquake with minimal missed and false detections for a community-engaged EEWS. Our results show that a citizen seismology-based EEWS is capable of detecting events of interest to EEW when selecting an appropriate earthquake detection algorithm. The study also suggests future research areas for community-engaged EEWSs, including dynamically changing P-wave detection thresholds and improving citizen seismologists’ user experience and involvement. [ABSTRACT FROM AUTHOR]

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs).

This article introduces a novel smart sensor that employs an advanced algorithm for earthquake early warning (EEW). The sensor utilizes a smart sampling technique to extract significant signal information, simplifying the process of inferring knowledge. The main objective is to assess the potential destructiveness of an incoming earthquake by analyzing the initial moments of the pressure wave and to generate an alert for prompt action, if necessary. This study includes the development and presentation of the proposed method, as well as performance evaluations using real seismic data obtained from freely accessible databases. These evaluations confirm the effectiveness of the proposed method in accurately estimating earthquake magnitudes. Furthermore, this article includes a comparison with a widely used EEW algorithm. The real-time functionality and interoperability of devices are crucial considerations in earthquake detection applications. The suitability and compatibility of the proposed method with the IEEE1451 family of standards are demonstrated and emphasized in this article.

With the development of the national earthquake intensity quick report and early warning project, more and more “earthquake early warning (EEW)” has come into public view. There are more and more questions about “what is EEW” at home and abroad along with a variety of interpretations of EEW techniques. This paper discusses the progress of earthquake early warning in China, the essence and limitation of EEW. The essence of EEW is that the earthquake observation has entered a new stage of intensive observation, and EEW is the alert for application of earthquake quick report from minutes to seconds. Due to the limitations of early warning blind area and inaccurate estimation of earthquake intensity in the practical application of EEW, the understanding of earthquake scientists on the application efficiency of EEW technology has been deepened and changed. They gradually realized the importance of strengthening the alert function of EEW. At the same time, EEW is a complex social engineering. Scientists need to guide the public to understand the limitations of EEW in order to play the role of disaster reduction effectively.

Earthquake early warning system (EEWS) plays an important role in detecting ground shaking during an earthquake and alerting the public and authorities to take appropriate safety measures, reducing possible damages to lives and property. However, the cost of high-end ground motion sensors makes most earthquake-prone countries unable to afford an EEWS. Low-cost Microelectromechanical systems (MEMS)-based ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust EEWS. This paper contributes to advancing Earthquake early warning (EEW) research by conducting a literature review investigating different methods and approaches to building a low-cost EEWS using MEMS-based sensors in different territories. The review of 59 articles found that low-cost MEMS-based EEWSs can become a feasible solution for generating reliable and accurate EEW, especially for developing countries and can serve as a support system for high-end EEWS in terms of increasing the density of the sensors. Also, this paper proposes a classification for EEWSs based on the warning type and the EEW algorithm adopted. Further, with the support of the proposed EEWS classification, it summarises the different approaches researchers attempted in developing an EEWS. Following that, this paper discusses the challenges and complexities in implementing and maintaining a low-cost MEMS-based EEWS and proposes future research areas to improve the performance of EEWSs mainly in 1) exploring node-level processing, 2) introducing multi-sensor support capability, and 3) adopting ground motionbased EEW algorithms for generating EEW. [ABSTRACT FROM AUTHOR]

Earthquake early warning system (EEWS) plays an important role in detecting ground shaking during an earthquake and alerting the public and authorities to take appropriate safety measures, reducing possible damages to lives and property. However, the cost of high-end ground motion sensors makes most earthquake-prone countries unable to afford an EEWS. Low-cost Microelectromechanical systems (MEMS)-based ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust EEWS. This paper contributes to advancing Earthquake early warning (EEW) research by conducting a literature review investigating different methods and approaches to building a low-cost EEWS using MEMS-based sensors in different territories. The review of 59 articles found that low-cost MEMS-based EEWSs can become a feasible solution for generating reliable and accurate EEW, especially for developing countries and can serve as a support system for high-end EEWS in terms of increasing the density of the sensors. Also, this paper proposes a classification for EEWSs based on the warning type and the EEW algorithm adopted. Further, with the support of the proposed EEWS classification, it summarises the different approaches researchers attempted in developing an EEWS. Following that, this paper discusses the challenges and complexities in implementing and maintaining a low-cost MEMS-based EEWS and proposes future research areas to improve the performance of EEWSs mainly in 1) exploring node-level processing, 2) introducing multi-sensor support capability, and 3) adopting ground motion-based EEW algorithms for generating EEW.

Digital Twins (DT) models are gaining special attention in the management and maintenance of facilities. The quality of data contained in these models may be enhanced by the use of processed information coming from long-term Structural Health Monitoring (SHM). In this case real time processing and updating in systems using sensor networks for SHM need low latency and reliable communication. This paper presents a solution for exploiting DT models for SHM and early warning solutions improvement. The case study scenario resides within the 5G experimentation in the city of L'Aquila and it exploits a highly adaptable sensor board and a 5G Multi-Access Edge Computing architecture. [ABSTRACT FROM AUTHOR]

Earthquake early warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake risk. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary research method of smartphone-based earthquake early warning has emerged. Some smartphone-based earthquake early warning systems have applied signal processing techniques and machine learning algorithms to the sensor data recorded by smartphones for better monitoring earthquakes. But it is challenging to collect abundant phone-recorded seismic data for training related machine learning models and selecting appropriate features for these models. One alternative way to solve this problem is to transform the data recorded by seismic networks into phone-quality data. In this paper, we propose such a transformation method by learning the differences between the data recorded by seismic networks and smartphones, in two scenarios: phone fixed and free located on tables, respectively. By doing this, we can easily generate abundant phone-quality earthquake data to train machine learning models used in EEW systems. We evaluate our transformation method by conducting various experiments, and our method performs much better than existing methods. Furthermore, we set up a case study where we use the transformed records to train machine learning models for earthquake intensity prediction. The results show that the model trained by using our transformed data produces superior performance, suggesting that our transformation method is useful for smartphone-based earthquake early warning. [ABSTRACT FROM AUTHOR]

Warning systems are essential for providing people with information so they can take protective action in response to perils. Systems need to be human-centered, which requires an understanding of the context within which humans operate. Therefore, our research sought to understand the human context for Earthquake Early Warning (EEW) in Aotearoa New Zealand, a location where no comprehensive EEW system existed in 2019 when we did this study. We undertook a survey of people's previous experiences of earthquakes, their perceptions of the usefulness of a hypothetical EEW system, and their intended responses to a potential warning (for example, Drop, Cover, Hold (DCH), staying still, performing safety actions). Results showed little difference in perceived usefulness of an EEW system between those with and without earthquake experience, except for a weak relationship between perceived usefulness and if a respondent's family or friends had previously experienced injury, damage or loss from an earthquake. Previous earthquake experience was, however, associated with various intended responses to a warning. The more direct, or personally relevant a person's experiences were, the more likely they were to intend to take a useful action on receipt of an EEW. Again, the type of experience which showed the largest difference was having had a family member or friend experience injury, damage or loss. Experience of participation in training, exercises or drills did not seem to prompt the correct intended actions for earthquake warnings; however, given the hypothetical nature of the study, it is possible people did not associate their participation in drills, for example, with a potential action that could be taken on receipt of an EEW. Our analysis of regional differences highlighted that intentions to mentally prepare on receipt of a warning were significantly higher for Canterbury region participants, most likely related to strong shaking and subsequent impacts experienced during the 2010–11 Canterbury Earthquake Sequence. Our research reinforces that previous experience can influence earthquake-related perceptions and behaviors, but in different ways depending on the context. Public communication and interventions for EEW could take into consideration different levels and types of experiences of the audience for greater success in response.

In this study we implemented and tested the Earthquake Early Warning system PRESTo (PRobabilistic and Evolutionary early warning System, Satriano et al., 2011) on the Greek Ionian islands of Lefkada, Zakynthos and Kefalonia. PRESTo is a free and open source platform for regional Earthquake Early Warning developed at the University of Naples Federico II, which is currently under experimentation in Southern Italy, in the area covered by the Irpinia Seismic Network. The three Ionian islands selected for this study are located on the North-Western part of the Hellenic trench. Here the seismicity rate and the seismic hazard, coupled with the vulnerability of existing critical infrastructures, make this region among the highest seismic risk areas in Europe, where the application of Earthquake Early Warning systems may become a useful strategy to mitigate the potential damage caused by earthquakes. Here we studied the feasibility of implementing an Earthquake Early Warning system on an existing seismic network, which was not specifically made for earthquake early warning purposes, and evaluated the performance of the system, using a data set of real-earthquake recordings. We first describe the technical details of the implementation of PRESTo in the area of interest, including the preliminary parameter configuration and the empirical scaling relationship calibration. Then we evaluated the performance of the system through the off-line analysis of a database of real earthquake records belonging to the most recent M > 4.0 earthquakes occurred in the area. We evaluated the performance in terms of source parameter estimation (location, magnitude), accuracy of ground shaking prediction and lead-time analysis. Finally, we show the preliminary results of the real-time application of PRESTo, performed during the period 01–31 July 2019.

The PLUM (Propagation of Local Undamped Motion) earthquake early warning (EEW) algorithm differs from typical source‐based EEW algorithms as it predicts shaking directly from observed shaking without first deriving earthquake source information (e.g., magnitude and epicenter). Here, we determine optimal PLUM event detection thresholds for U.S. West Coast earthquakes using two data sets: 558 M3.5+ earthquakes (California, Oregon, Washington; 2012–2017) and the ShakeAlert test suite of historic and problematic signals (1999–2015). PLUM computes Modified Mercalli Intensity (IMMI) using velocity and acceleration data, leveraging co‐located sensors to avoid problematic signals. An event detection is issued when the observed IMMI exceeds a given threshold(s). We find a two‐station detection method using IMMI trigger thresholds of 4.0 and 3.0 for the first and second stations, respectively, is optimal for detecting M4.5+ earthquakes. PLUM detected 79 events in the 2012–2017 data set, reporting (not including telemetry or alert dissemination) detection times on par, and sometimes faster than current EEW methods (mean 8 s; median 6 s). As expected, detection times were slower for the older 1999–2015 earthquakes (N = 21; mean 11 s; median 6 s) when station coverage was sparser. Of the 31 PLUM detected M5+ events (10 2012–2017; 21 1999–2015), theoretically 20 (∼65%) could provide timely warnings. PLUM issued no false detections and avoided issuing detections for all calibration/anomalous signals, regional and teleseismic events. We conclude PLUM can successfully identify IMMI 4+ shaking from local earthquakes and could complement and enhance EEW in the U.S. Plain Language Summary: Earthquake early warning (EEW) detection schemes require (1) ample seismic information to identify where large ground motions are underway; (2) determining if these ground motions are significant enough to issue a detection; and (3) detecting large ground motions in a timely fashion. Some EEW methods estimate earthquake source parameters like magnitude and location and then input those parameters into a ground‐motion prediction equation, while other methods use observations of the ground motions to directly forecast shaking. We explore the latter approach using the PLUM (Propagation of Local Undamped Motion) method to detect earthquakes that produce shaking above a target value. In this work, we test PLUM's ability to detect earthquakes using two data sets: 558 earthquakes magnitude 3.5 and above from California, Oregon, and Washington (2012–2017) and a test suite of historic and problematic signals (1999–2015) curated by ShakeAlert. We find a two‐station detection method is preferred over a one‐station method as two‐stations can greatly minimize false detections. The PLUM method is also 100% successful at avoiding non‐earthquake anomalous signals and can successfully differentiate ground shaking from local and distant earthquakes. We conclude that PLUM may be a promising candidate for integration into the U.S. EEW system. Key Points: Propagation of Local Undamped Motion (PLUM) detects offshore events that produce at least moderate onshore ground motions, including an event problematic for other algorithmsPLUM's detection times are as timely, and sometimes faster, than other earthquake early warning detection timesPLUM correctly avoids erroneous detections for all teleseismic, calibration pulses, and anomalous signals in the test suite [ABSTRACT FROM AUTHOR]

In this study, we first discuss the current status and issues of disaster management education in the context of special support education in Japan, in view of the casualties of those with disabilities during major past earthquakes in Japan. We highlight that there are very few examples of practical implementation of, instructional material for, or previous studies on disaster management education for disabled children, or an established systematic instructional method. As a result, disaster management education tailored to the specific type of disability has been implemented on a school-to-school basis among Special Support Schools for children with disabilities. In many cases, teacher-led evacuation drills have been considered disaster management education. This is an indication that the disaster management education currently practiced in Special Support Schools is inadequate to achieve the goal of "fostering the attitude of acting on one's initiative" as set forth by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). In view of the situation in Japan, where casualties due to natural disasters continue to occur frequently since the Great East Japan Earthquake, it is urgent that we promote practical disaster management education to foster the Zest for Life among disabled children. This paper is a case study of disaster management education that targets those with intellectual disabilities, which is the largest reported disability type among children enrolled in Special Support Schools in Japan. We applied the ADDIE (Analyze, Design, Develop, Implement, Evaluate) process in instructional design to develop an earthquake disaster management program designed to heighten the capacity of disabled children to foresee and circumvent danger to themselves, so as to protect their lives from large earthquakes which occur frequently in Japan. Specifically, the objective is to apply the earthquake disaster management education program, developed by the authors in a previous study, to children with intellectual disabilities. To this end, we implemented the program at the target school and verified its educational effect while taking into consideration the degree or condition of disability and the learning characteristics of the intellectually disabled and developed a valid program for intellectually disabled children. The program allows the teachers of Special Support Schools to practice disaster management education in the context of daily classroom study with students without the need to dispatch a disaster management expert to the school each time a program is implemented. Additionally, the program can be customized by the onsite teacher for individual schools, which can lead to a systematic program in disaster management education. In addition, we propose a framework to establish a network of stakeholders, including disaster management experts or organizations and educational institutions to effectively and strategically promote disaster management education. This framework makes it possible to implement the present program the most impactful way, and to maximize the benefits to the schools in Tochigi prefecture. [ABSTRACT FROM AUTHOR]

The main goal of present study is to test the functionality of an earthquake early warning (EEW) system (a life-saving tool), in India using synthesized data and recorded earthquake data from Taiwan. In recent time, India set up an EEW system in the central seismic gap along the Himalayan Belt, consisting of about 100 low-cost P-Alert instruments. The area, where these instruments are installed, is highly sensitive to the seismic risk with the potential of strong, major and great earthquakes. In the absence of recorded data from the Himalayas required for analysis of such system, we take advantage of recorded waveforms from Taiwan, to test the EEW system. We selected Taiwanese stations in good accordance with the Indian sensor network, to have a best fit in terms of inter station spacing. Finally, the recorded waveforms are passed through Earthworm software using tankplayer module. The system performs very well in terms of earthquake detection, P-wave picking, earthquake magnitude and location (using previously estimated regressions). Pd algorithm has been tested where the peak amplitude of vertical displacement is used for estimating magnitudes using previously regressed empirical relationship data. For the earthquakes located between Main Boundary Thrust and Main Central Thrust along with a matching instrumentation window, a good estimate of location, as well as magnitude is observed. The approach based on Pd for estimating magnitude works perfectly as compared to τc approach, which is more sensitive to signal-to-noise ratio. To make it more region specific, we generated synthetic seismograms from the epicenters of historical Chamoli (1999) and Uttarkashi (1991) earthquakes at EEW stations in India and checked the functionality of EEW. While placing these earthquakes within the instrumentation window, a good approximation of earthquake location and magnitude is obtained by passing these generated waveforms. The parameters used to judge the performance of EEW system included the time taken by the system in issuing warning after the confirmation of the occurrence of damaging earthquake and the lead time (time interval between the issuing of warning and arrival of damaging earthquake ground motion at a particular location). High lead times have been obtained for the plainer regions including thickly populated regions of Gangetic plains, such as Delhi National Capital Region according to the distance from the epicenter, which are the main target of EEW system. [ABSTRACT FROM AUTHOR]

The stability and durability of the energy supply for sensing nodes in a wireless sensors network (WSN) is an important research issue and requiring improvement for WSNs in structural health monitoring (SHM) systems. Having control sensors periodically enter a low-power mode or sleep state is commonly used to reduce energy consumption. If a node receives a sampling command but the node remains in listening-time cycle, the sampling process will be delayed. Hence, the capabilities of radio triggering can improve stability and durability when integrated with an external low-power circuit attached to sensing nodes. Transmitting a wake-up command when specific start-up conditions are met to quickly awaken sensing nodes to work wirelessly is also an effective approach. The objective of this work is to integrate sensing field-type p-wave technology to construct an intelligent energy economical WSN with sentry nodes embedded with an earthquake early warning (EEW) system. Sentry nodes are integrated with WSN gateways and employed to link and synchronize all sensing nodes in advance through seismic prediction and radio-triggering technology. In this mode, the average power consumed was measured at 350 μA. A sensor will be more effective in measuring structural responses after an earthquake by increasing available sleep time. [ABSTRACT FROM AUTHOR]

Earthquake early warning systems (EEWSs) that rapidly trigger risk-reduction actions after a potentially-damaging earthquake is detected are an attractive tool to reduce seismic losses. One brake on their implementation in practice is the difficulty in setting the threshold required to trigger pre-defined actions: set the level too high and the action is not triggered before potentially-damaging shaking occurs and set the level too low and the action is triggered too readily. Balancing these conflicting requirements of an EEWS requires a consideration of the preferences of its potential end users. In this article a framework to define these preferences, as part of a participatory decision making procedure, is presented. An aspect of this framework is illustrated for a hypothetical toll bridge in a seismically-active region, where the bridge owners wish to balance the risk to people crossing the bridge with the loss of toll revenue and additional travel costs in case of bridge closure. Multi-attribute utility theory (MAUT) is used to constrain the trigger threshold for four owners with different preferences. We find that MAUT is an appealing and transparent way of aiding the potentially controversial decision of what level of risk to accept in EEW. [ABSTRACT FROM AUTHOR]

Development of earthquake early warning system (EEWS) is in advanced stage in different parts of the world including India. The success of EEWS for mitigating seismic risk and saving human lives has been well documented in Mexico, Japan, and Taiwan, where the alert is issued to the public. Taking advantage of the recorded ground motion data from the network of Institute of Seismological Research (ISR), India, with magnitude range 3.0–5.2, we investigated correlations between various ground motion parameters like peak ground acceleration (PGA), peak ground velocity (PGV), peak ground displacement (PGD), Pa, Pv, Pd, and τc.Three- to 5-s time windows are considered to measure Pa, Pv, Pd, and τc from the vertical component of the waveforms. Linear regression analysis is performed at various time steps (3- to 5-s time interval). The results show that considering 4- and 5-s windows exhibits good relationships compared with the 3-s window, which shows more scattering. These empirical relationships using τc and M as well as Pd and M are very helpful in determining the earthquake magnitude and subsequently taking steps toward risk assessment. [ABSTRACT FROM AUTHOR]