On-site Earthquake Early Warning System Research Articles

P-Alert earthquake early warning system: case study of the 2022 Chishang earthquake at Taitung, Taiwan

A series of earthquakes that struck Taiwan's southern Longitudinal Valley on September 17 and 18, 2022 severely damaged several buildings in Taitung and Hualien. The Chishang earthquake, which had a magnitude of ML 6.8 and a large foreshock with a magnitude of ML 6.6 the day before, was the mainshock in this sequence. The strongest intensity reported in the epicentral region during this earthquake sequence, which was 6 + , is the highest ever recorded since the Central Weather Bureau (CWB, renamed as the Central Weather Administration since September 15, 2023) revised its seismic intensity scale. National Taiwan University (NTU) has operated a low-cost earthquake early warning (EEW) system known as the P-Alert for a decade. In this study, we demonstrate the performance of the P-Alert network during the 2022 Chishang earthquake and the largest foreshock. The P-Alert network plotted shake maps during these earthquakes that displayed various values within 5 min. The high shaking areas on these maps were in good agreement with observed damages during this earthquake, providing valuable insights into rupture directivity, a crucial component of earthquake engineering. Individual P-Alert stations acted as on-site EEW systems and provided a lead time of 3–10 s within the blind zone of CWB. For the ML 6.8 mainshock, there was a lead time of at least 5 s, even up to 10 s, demonstrating their effectiveness in the blind zone. The P-Alert regional EEW system provided the first report about 9 s and 7 s after the mainshock and the largest foreshock occurrence, respectively, with estimated magnitudes of 5.74 and 5.67. The CWB system estimated magnitudes of 6.72 and 6.16 in the first report, respectively, about 7 s and 9 s after the earthquake occurrence. The timeliness of the two systems were not significantly different. Despite the effectiveness of the P-Alert network, data loss due to connection interruptions prompted us to develop a new compact data logger for improved data availability.

On-Site Earthquake Early Warning System as an Alternative Earthquake Mitigation Solution in Indonesia

The Banten earthquake, which had a magnitude of 6.6 on January 14, 2022, damaged 3,078 houses. That<br />number consisted of 395 heavily damaged units, 692 moderately damaged units and 1,991 lightly damaged units<br />(Tempo.co, 2022). The Banten earthquake was a strong earthquake where the magnitude was greater than a scale of<br />5. Damage to houses caused by the earthquake occurred in most single-story houses or low-rise buildings. Given the<br />large number of one-story houses that are damaged every time a major earthquake occurs in Indonesia, there needs to<br />be appropriate mitigation measures to reduce the risk of earthquake disasters, especially for human casualties. An<br />On-site Earthquake Early Warning System (On-site EEWS) can be an alternative in reducing victims of the disaster.<br />This earthquake early warning system has sensors that are installed on the site of building houses and can predict<br />strong earthquake waves that are destructive in nature (S/Secondary Waves) through P/Primary Waves that arrive<br />early in about 10-20 seconds. This time is sufficient for evacuation for the occupants of a one-story house if the early<br />warning alarm is properly responded to. This early warning radius can reach 20 km from the on-site EEWS location<br />considering that this area has relatively the same vibration effect. Currently, Indonesia through the BMKG is<br />developing EEWS as a part of the existing earthquake mitigation system. The purpose of this study is to describe the<br />application of an on-site earthquake early warning system as an alternative solution for earthquake mitigation in<br />Indonesia. This study evaluates several EEWS applications in the literature to find the best alternative to be applied<br />in Indonesia. The critical factors for on-site implementation of the EEWS discussed in this paper are compared with<br />the Taiwan regional EEWS. Based on the existing validation, the on-site EEWS has an 80% accuracy rate in<br />predicting the intensity level of a strong earthquake, capable to automatically send an alarm message within 3<br />seconds and providing a warning time of at least 8 seconds before a destructive peak S wave arrives.

Unveiling the potential: preliminary result on an AI-based onsite earthquake early warning system deployment in Denpasar City

Due to Indonesia’s geographical location within the Pacific Ring of Fire, it is susceptible to earthquake occurrences. Therefore, the implementation of an Earthquake Early Warning (EEW) System is crucial to mitigate the impact of earthquakes. This study focused on deploying an on-site EEW system on Bali Island, an Indonesian region known for its elevated seismic activity. The study evaluated the system’s performance over five months in 2023. Throughout this observation period, the AI-based on-site EEW system successfully detected the earthquake’s P-wave before the occurrence of stronger seismic waves, such as the peak ground acceleration (PGA). It provided a warning time of up to 75 seconds and accurately predicted the intensity of the impending earthquake. These outcomes meet the fundamental requirements for an effective EEW system. The results obtained from the deployed AI-based on-site EEW system demonstrate the potential for wider implementation of such systems across Indonesia.

The Urban Seismic Observatory of Catania (Italy): A Real-Time Seismic Monitoring at Urban Scale

We describe the first dense real-time urban seismic–accelerometric network in Italy, named OSU-CT, located in the historic center of Catania. The city lies in the region with the greatest danger, vulnerability, and earthquake exposure in the entire Italian territory. OSU-CT was planned and realized within the project called EWAS “an Early WArning System for cultural heritage”, aimed at the rapid assessment of earthquake-induced damage and the testing of an on-site earthquake early warning system. OSU-CT is mainly based on low-cost instrumentation realized ad hoc by using cutting-edge technologies and digital MEMS (micro-electro-mechanical systems) triaxial accelerometers with excellent resolution and low noise. Twenty of the forty scheduled stations have already been set up on the ground floor of significant historic public buildings. In order to assess the performance of an earthquake early warning (EEW) on-site system, we also installed wide-band velocimeters (ETL3D/5s) in three edifices chosen as test sites, which will be instrumented for a structural health monitoring (SHM). In addition to several laboratory and field validation tests on the developed instruments, an effective operational test of OSU-CT was the Mw 4.3 earthquake occurring on 23 December 2021, 16 km west, south-west of Catania. Peak ground accelerations (4.956 gal to 39.360 gal) recorded by the network allowed obtaining a first urban shakemap and determining a reliable distribution of ground motion in the historical center of the city, useful for the vulnerability studies of the historical edifices.

Estimation of Earthquake Early Warning Parameters for Eastern Gulf of Corinth and Western Attica Region (Greece). First Results.

The main goal of an Earthquake Early Warning System (EEWS) is to alert before the arrival of damaging waves using the first seismic arrival as a proxy, thus becoming an important operational tool for real-time seismic risk management on a short timescale. EEWSs are based on the use of scaling relations between parameters measured on the initial portion of the seismic signal after the arrival of the first wave. To explore the plausibility of EEWSs around the Eastern Gulf of Corinth and Western Attica, amplitude and frequency-based parameters, such as peak displacement (Pd), the integral of squared velocity ( and the characteristic period (τc), were analyzed. All parameters were estimated directly from the initial 3 s, 4 s, and 5 s signal windows (tw) after the P arrival. While further study is required on the behavior of the proxy quantities, we propose that the parameter and the peak amplitudes of the first seconds of the P waves present significant stability and introduce the possibility of a future on-site EEWS for areas affected by earthquakes located in the Eastern Gulf of Corinth and Western Attica. Parameters related to regional-based EEWS need to be further evaluated.

Deep Learning-Based, Real-Time, False-Pick Filter for an Onsite Earthquake Early Warning (EEW) System

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

Could a Decentralized Onsite Earthquake Early Warning System Help in Mitigating Seismic Risk in Northeastern Italy? The Case of the 1976 Ms 6.5 Friuli Earthquake

Abstract In May 1976, a devastating earthquake of magnitude Ms 6.5 occurred in Friuli, Italy, resulting in 976 deaths, 2000 injured, and 60,000 homeless. It is notable that, at the time of the earthquake, only one station was installed in the affected region. The resulting lack of information, combined with a dearth of mitigation planning for responding to such events, lead to a clear picture of the impact of the disaster being available only after a few days. This region is now covered by nearly 100 seismological and strong-motion stations operating in real time. Furthermore, 30 average-cost strong-motion stations have been recently added, with the goals of improving the density of real-time ground-motion observations and measuring the level of shaking recorded at selected buildings. The final goal is to allow rapid impact estimations to be made to improve the response of civil protection authorities. Today, considering the higher density seismological network, new efforts in terms of the implementation and testing of earthquake early warning systems as a possible tool for mitigating seismic risk are certainly worthwhile. In this article, we show the results obtained by analyzing in playback and using an algorithm for decentralized onsite earthquake early warning, broadband synthetic strong-motion data calculated at 18 of the stations installed in the region, while considering the magnitude and location of the 1976 Friuli earthquake. The analysis shows that the anisotropy of the lead times is related not only to the finite nature of the source but also to the slip distribution. A reduction of 10% of injured persons appears to be possible if appropriate mitigating actions are employed, such as the development of efficient automatic procedures that improve the safety of strategic industrial facilities.

Investigation of site amplification factors for S- and P-waves from spectral inversions in the Tokyo metropolitan area, Japan—for application to earthquake early warnings

We performed spectral inversions using ground motion data observed by K-NET, KiK-net, SK-net, and the earthquake observation network of the East Japan Railway Company in the Tokyo metropolitan area, Japan, to evaluate the effects of site amplification factors of both S- and P-waves for the future use of an on-site earthquake early warning system based on predicted S-wave amplitudes from observed P-wave amplitudes. The source and path effects from the inversions were similar to those estimated by previous studies. The spatial distributions of the site amplification factors of the S- and P-waves depend on the geological conditions, especially for the S-waves. The site amplification factors of the S- and P-waves nearly agree with those calculated with a 1D reverberation theory using existing velocity models from KiK-net. The site amplification ratios of the S-waves to the P-waves show a decrease in the amplitudes at frequencies greater than 2–5 Hz, especially on the plateaus and in the coastal areas covered with thick soft layers because the site amplification factors of the S-waves are small in this frequency range and those of the P-waves are nearly constant over the entire frequency range. Further, the remarkable peaks of the site amplification factors for the S-waves are preserved in the site amplification ratios of the S-waves to the P-waves at sites in the valleys and on the boundaries between the plateaus and the lowlands.

Performance of On-Site Earthquake Early Warning System Using Strong-Motion Records from Recent Earthquakes

AbstractThe method of on-site earthquake early warning was proposed in the past decade to enhance seismic preparedness and safety measures for areas close to the earthquake epicenter. The method le...

Developing an energy-efficient and low-delay wake-up wireless sensor network-based structural health monitoring system using on-site earthquake early warning system and wake-on radio

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.

Reliability assessment on earthquake early warning: A case study from Taiwan

Earthquake early warning (EEW) has been implemented in several regions around the world. However, because of natural randomness and uncertainty, false alarm and missed alarm can be expected in EEW. The key scope of this study is to evaluate the reliability of an on-site EEW in Taiwan, by testing the system's algorithm with 17,836 earthquake data from 1999 to 2013. The analysis shows that the on-site EEW system, empirically speaking, should have a false-alarm probability of 2.5%, and a missed-alarm probability of 14.1%. Considering missed alarm should be more critical to EEW, a new algorithm that could reduce the system's missed-alarm occurrences to 6% is also discussed in this paper.

Performance of a Low‐Cost Earthquake Early Warning System (P‐Alert) during the 2016ML 6.4 Meinong (Taiwan) Earthquake

On 5 February 2016, a moderate earthquake occurred in southwestern Taiwan with M L 6.4 and a focal depth of 16.7 km. This earthquake caused damage to a few buildings and 117 casualties. A low‐cost earthquake early warning (EEW) system ( P ‐alert) is in operation for the purpose of EEW and for providing near‐real‐time shake maps. During this event, a detailed shaking map was generated by the P ‐alert system within 2 min after the earthquake occurrence, and the high shaking regions strongly correlated with the locations in which the damage and casualties occurred. In the field, individual P ‐alert devices also serve as onsite EEW systems using P ‐wave information. The individual P ‐alert provided a 4–8 s lead time before the arrival of violent shaking in the damaged regions. For regional EEW, both the Central Weather Bureau (CWB, official agency) and the P ‐alert system responded very well. Currently, regional warnings in Taiwan are only provided to cities at epicentral distances of 50 km or more by the CWB. For cities within a 50‐km epicentral distance, the P ‐alert system could be useful for providing onsite EEW. The performance of the P ‐alert network during this earthquake proves the efficiency of this real‐time, low‐cost network in terms of early warning (regional and onsite), near‐real‐time shake maps, rapid reports, and strong‐motion data for research purposes.

Development of an integrated onsite earthquake early warning system and test deployment in Zhaotong, China

Earthquake Early Warning System (EEWS) is one of the effective ways to mitigate earthquake damage and can provide few seconds to tens of seconds of advanced warning time of impending ground motions, allowing for mitigation measures to be taken in the short term. In the present paper, we develop an integrated onsite EEWS called EDAS-MAS, which is based on the physical characteristics of the P-wave velocity greater than S-wave velocity. The instrument is a single device which includes a 3-channel MEMS accelerometer, a data acquisition unit, seismological processing, and three types of alarms. Two types of magnitudes are computed by using τc-Pd values and the estimated hypocentral distance. It can directly raise the warning to the public when the magnitudes exceed the predefined thresholds, providing longer effective warning time and reducing the “blind zone” range. Although a majority of the reported events were small earthquakes and a relative high false alarms rate existed in the test, the results have shown the capabilities of the prototype EDAS-MAS for EEWS and are of practical importance for the design and optimization of the system.

Magnitude-scaling relations using period parameters τc and τpmax, for Tehran region, Iran

In this study, the first step towards establishing an onsite earthquake early warning system (EWS) in the Tehran region is presented. The system uses the period parameters tau(max)(p) and tau(c) from the first 3 s of the vertical and horizontal components of a P wave, separately and combined. Various regression relations between the magnitude and period parameters were determined for different seismic networks operating in the study area. The data set used in this study contains small ground motions including 194 events with magnitudes between M-L 2.5 and 4.6 located within approximately 80 km from the epicentre in the Tehran region. The SDs of the magnitude-scaling relations for all the component categories (vertical, horizontal and total components) based on the tau(max)(p) and tau(c) approaches were estimated to be on the order of +/- 1.0 and +/- 1.1 unit of magnitude, respectively. These relations were determined from the small magnitude range of the velocity records (M-L 2.5-4.6) as input seismograms. Additional tests were conducted to verify the reliability and robustness of the determined magnitude-scaling relations using acceleration records from the 2002 June 22, M-L 6.5 Changureh-Avaj earthquake; 2004 May 28, M-L 6.1 Firoozabad-Koojour earthquake; 2009 October 17, M-L 3.9 Shahre-Rey earthquake and 2011 February 20, M-L 4.1 Sharif-Abad earthquake; the first two events (Changureh-Avaj and Firoozabad-Koojour) occurred outside the study area. Among the various regression scaling relations obtained, the estimated magnitude based on the tau(max)(p) approach using the vertical components yielded the most stable and reliable results of 6.4 (+/- 0.4), 5.9 (+/- 0.4), 3.3 (+/- 0.5) and 3.8 (+/- 0.3) for the Changureh-Avaj, Firoozabad-Koojour, Shahre-Rey and Sharif-Abad earthquakes, respectively. The magnitudes estimated using the tau(c) method exhibited more scatter with higher SDs than those using the vertical components using the tau(max)(p) approach. Our results also indicate that using the horizontal components produces larger SDs, which may be attributed to the larger site effects; however, the horizontal components can be used as auxiliary available data to provide more constrained information for a multilevels pilot alarm system and to reduce the number of missed or false alarms. The main uncertainties in the proposed magnitude-scaling relations result from the absence of any large earthquakes and poor station distributions in the study area. However, the results presented in this study can be used as a pilot onsite earthquake EWS in the Tehran region.

The uncertainties of a Pd3–PGV onsite earthquake early warning system

Earthquake early warning systems have been developed to help minimize the loss of life and other damages resulting from catastrophic earthquakes. In the past several decades, these systems have evolved to function using a variety of methodologies. This study introduces an onsite system developed from a correlation between the precursor measurement (Pd3) and the peak ground acceleration (PGV) from 780 historical earthquake events recorded in Taiwan, Japan and South California. The study analyzes and integrates the uncertainty of the onsite system, establishing the statistical relationship between the PGV forecast, the Pd3 precursor, and the exceedance probability (PE). A method is presented for forecasting the PGV by scaling the Pd3. Furthermore, this study also suggests a decision-making model to determine whether or not the early warning notification should be issued, based on factors such as the uncertainty of the early warning system, the magnitude of the precursor, and warning recipient characteristics.