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Recent studies in the earthquake magnitude‐frequency relation (b‐value) found that the b‐value can decrease with time before large earthquakes in the source area. Such a decrease even coincided with a burst of foreshocks immediately before the earthquakes implying a preslip migrating toward the nucleation zone. We systematically survey the b‐value variations in space and time in the source area of 17 earthquakes with magnitudes greater than ML 6.0 in Taiwan. We relocated the earthquakes released by the Central Weather Administration of Taiwan since 2012 as timing can provide a sufficiently low completeness magnitude (MC) of 1.5. We carefully determine the spatiotemporal search criteria on seismicity in the source area of all targeted earthquakes. We surveyed the temporal b‐value systematically using moving time windows with a given number of earthquake events with the magnitudes greater than MC. We found the b‐value decreased clearly for 2 days before the 2018 ML 6.3 Hualien earthquake, coinciding with a burst of ML 5.8 earthquake and its aftershocks nearby as foreshocks. The foreshocks migrated updip toward the hypocenter of the 2018 ML 6.3 Hualien earthquake and revealed a second decrease in the b‐value 10 hours before it occurred. The b‐value precursor primarily comes from a rapid reduction of small earthquakes in the foreshocks and agrees with recent key findings of preslip. However, there is no strong correlation between the b‐value precursor and coseismic slip. Apart from this, the b‐values did not change significantly in most cases of earthquakes in Taiwan before and after they occurred. Plain Language Summary: Direct evidence for the precursor of a large earthquake is rare and difficult to be quantified accurately. Earthquake magnitude‐frequency relation (b‐value) is a classic observation implying stress accumulation on a seismogenic fault. Scientists found that the b‐value can decrease within a few days before an impending large earthquake in the source area. Taiwan is one of the regions undergoing frequent M ≥ 6.0 earthquakes and can map the b‐value in high resolution based on the relocated earthquakes. This study systematically surveyed the b‐value in the source area of seventeen ML ≥ 6.0 earthquakes in Taiwan, probing into b‐value precursor in the 8‐year time series. We found that the b‐value had a robust decrease 2 days before the 2018 ML 6.3 Hualien earthquake, coinciding with the initiation of foreshocks nearby and at a downdip of the hypocenter of the 2018 ML 6.3 Hualien earthquake. The foreshocks showed clear evidence of precursor as they migrated updip toward the hypocenter with a second decrease in the b‐value 10 hours before the earthquake occurred. We found no evidence of a b‐value precursor in most of the targeted earthquakes that agrees with the knowledge of precursor as happening episodically and unexpectedly. Key Points: We found a b‐value precursor that coincides with foreshocks for 2 days before the 2018 ML 6.3 Hualien earthquake in the source areaThe b‐value precursor correlates with foreshock distribution in the source area and is likely uncorrelated to the high coseismic slipThere is no significant change in the b‐value before M ≥ 6.0 earthquakes in Taiwan in a majority of the cases that we analyzed [ABSTRACT FROM AUTHOR]

Building an earthquake early warning (EEW) network requires the installation of seismic instruments around the seismogenic zone. Using low-cost sensors to build a seismic network for EEW and to generate shakemaps is a cost-effective way in the field of seismology. The National Taiwan University (NTU) network employing 762 P-Alert low-cost sensors based on micro-electro-mechanical systems (MEMS) technology is operational for almost the last 10 years in Taiwan. This instrumentation is capable of recording the strong ground motions of up to ± 2 g and is dense enough to record the near-field ground motion. The NTU system has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an onsite warning system, it is particularly useful for onsite warning. Using real-time seismic signals, each P-Alert device provided a 2–8 s warning time for the near-source earthquake regions situated in the blind zone of the Central Weather Bureau (CWB) regional EEW system, during the 2016 M w 6.4 Meinong and 2018 M w 6.4 Hualien earthquakes. The shakemaps plotted by the P-Alert dense network help to assess the damage pattern and act as key features in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Currently, the P-Alert network can provide peak ground acceleration (PGA), peak ground velocity (PGV), spectral acceleration ( S a ) at different periods, and CWB intensity shakemaps. Using shakemaps, it is found that PGV is a better indicator of damage detection than PGA. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries. [ABSTRACT FROM AUTHOR]

Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, $K$ -nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate. [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]

Taiwan is one of the leading developers of earthquake early warning (EEW) systems. The Central Weather Bureau has been the primary developer of the EEW system in Taiwan since 1993. In 2010, the National Taiwan University (NTU) developed an EEW system for research purposes using low-cost accelerometers. As of 2014, a total of 506 stations have been deployed and configured. The NTU system can provide earthquake information within 15 s of an earthquake occurrence. Thus, this system may provide early warnings for cities located more than 50 km from the epicenter. Additionally, the NTU system also has an onsite alert function that triggers a warning for incoming P-waves greater than a certain magnitude threshold, thus providing a 2-3 s lead time before peak ground acceleration for regions close to an epicenter. Detailed shaking maps are produced by the NTU system within one or two minutes after an earthquake. Regions of high shaking indicated by the shalking map can indicate locations of damage and casualties and help estimate the damage incurred. The direction of earthquake ruptures are also potentially identified based on detailed shaking maps and strong motion records of the NTU system. [ABSTRACT FROM AUTHOR]

Abstract: 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 (P E ). 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. [Copyright &y& Elsevier]

Abstract: The use of characteristic period τc and peak displacement amplitude Pd of the initial P wave in earthquake early warning (EEW) was proposed by Wu and Kanamori . Here we apply this approach to strong-motion records from a building sensor array installed in Taitung County, Taiwan. This building was damaged during the 2006 Mw =6.1 Taitung earthquake with a peak ground velocity (PGV) of up to 38.4cm/s at an epicentral distance of 14.5km. According to our analysis, the peak displacement amplitude Pd is a better indicator for the destructiveness of an earthquake than τc because τc is more sensitive to the signal-to-noise ratio (SNR) than Pd. In accordance with previous studies, only the structurally damaging Taitung earthquake generated a Pd value larger than 0.5cm (a threshold for identifying damaging events). Using Pd as an indicator for destructive earthquakes does not lead to missing or false alarms for EEW purposes. [ABSTRACT FROM AUTHOR]

Abstract: A new prototype earthquake early warning (EEW) system is being developed and tested using a real-time seismographic network currently in operation in Taiwan. This system is based on the Earthworm environment which carries out integrated analysis of real-time broadband, strong-motion and short-period signals. The peak amplitude of displacement in the three seconds after the P arrival, dubbed Pd , is used for the magnitude determination. Incoming signals are processed in real time. When a large earthquake occurs, P-wave arrival times and Pd will be estimated for location and magnitude determinations for EEW purpose. In a test of 54 felt earthquakes, this system can report earthquake information in 18.8±4.1s after the earthquake occurrence with an average difference in epicenter locations of 6.3±5.7km, and an average difference in depths of 7.9±6.6km from catalogues. The magnitudes approach a 1:1 relationship to the reported magnitudes with a standard deviation of 0.51. Therefore, this system can provide early warning before the arrival of S-wave for metropolitan areas located 70km away from the epicenter. This new system is still under development and being improved, with the hope of replacing the current operational EEW system in the future. [ABSTRACT FROM AUTHOR]

Abstract: Modern technology allows real-time seismic monitoring facilities to evolve into earthquake early warning (EEW) systems, capable of reducing deaths, injuries, and economic losses, as well as of speeding up rescue response and damage recovery. The objective of an EEW system is to estimate in a fast and reliable way the earthquake’s damage potential, before the strong shaking hits a given target. The necessary framework for EEW implementation is provided by the observed relationships between different parameters measured on the signal onsets and the final earthquake size. The implication of these observations on the physics of fracture processes has given rise to a significant debate in the seismological community. Currently, EEW systems are implemented or under testing in many countries of the world, and different methodologies and procedures have been studied and developed. The leading experience of countries like Japan or Mexico shows that, with a proper education of population and end-users, and with the design of real-time systems for the reduction of vulnerability/exposure, EEW can be an effective approach to the mitigation of the seismic risk at short time-scales. [ABSTRACT FROM AUTHOR]

Seismic instrumentation for earthquake early warnings (EEWs) has improved significantly in the last few years, considering the station coverage, data quality, and the related applications. The official EEW system in Taiwan is operated by the Central Weather Bureau (CWB) and is responsible for issuing the regional warning for moderate-to-large earthquakes occurring in and around Taiwan. The low-cost micro-electro-mechanical system (MEMS)-based P-Alert EEW system is operational in Taiwan for on-site warnings and for producing shakemaps. Since 2010, this P-Alert system, installed by the National Taiwan University (NTU), has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an on-site warning system, it is particularly useful for on-site warning. Using real-time seismic signals, each P-Alert system can provide a 2–8 s-long warning time for the locations situated in the blind zone of the CWB regional warning system. The shakemaps plotted using this instrumentation help to assess the damage pattern and rupture directivity, a key feature in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Earlier, the network provided only peak ground acceleration (PGA) shakemaps, but has now been updated to include peak ground velocity (PGV), spectral acceleration ( S a ) at different periods, and CWB intensity maps. The PGA and PGV shakemaps plotted using this network have proven helpful in establishing the fact that PGV is a better indicator of damage detection than PGA. This instrumentation is also useful in structural health-monitoring and estimating co-seismic deformations. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries. [ABSTRACT FROM AUTHOR]

Abstract: Two recent catastrophic earthquakes that struck the Marmara Region on 17 August 1999 (Mw =7.4) and 12 November 1999 (Mw =7.2) caused major concern about future earthquake occurrences in Istanbul and the Marmara Region. As a result of the preparations for an expected earthquake may occur around Istanbul region, an earthquake early warning system has been established in 2002 with a simple and robust algorithm, based on the exceedance of specified thresholds of time domain amplitudes and the cumulative absolute velocity (CAV) levels (Erdik et al., 2003 ). In order to improve the capability of Istanbul earthquake early warning system (IEEWS) for giving early warning of a damaging earthquake in the Marmara Region, we explored an alternative approach with the use of a period parameter (τc ) and a high-pass filtered vertical displacement amplitude parameter (Pd) from the initial 3s of the P waveforms as proposed by Kanamori (2005) and Wu and Kanamori (2005) . The empirical relationships both between τc and moment magnitude (Mw ), and between Pd and peak ground velocity (PGV) for the Marmara Region are presented. These relationships can be used to detect a damaging earthquake within seconds after the arrival of P waves, and can provide on-site warning in the Marmara Region. [ABSTRACT FROM AUTHOR]

Earthquake early warning (EEW) plays an important role in earthquake disaster mitigation. There are two types of EEW system, regional and on-site. On-site EEW systems analyze initial part of the seismic waves from the P-waves to predict later ground motion from the S-waves and surface waves. In recent years, an on-site EEW method based on fixed peak displacement (Pd) threshold is developed. Although the method consistently provides effective warnings in Taiwan, several studies suggest that its strong filter dependence might introduce extra biases to the system. Also, the fixed Pd threshold suffers from the inevitable trade-off between a false alarm and a missed alarm. In order to overcome the abovementioned problems of fixed Pd threshold method, we utilize techniques in machine learning and develop a new method of on-site early warning. Owing to the property that convoluted-neural-network (CNN) will automatic sampling on different frequencies, the unfiltered seismic signal itself is sufficient to derive a warning threshold. Also, multi-layered neural-network is capable of forming a complex non-linear threshold to reduce both the false alarms and missed alarms. As an example, we collect 21 medium to large inland earthquakes in Taiwan. We compare the performance between the proposed method and the fixed Pd threshold method. The result shows that the proposed method outperforms in both false alarm rate (0.01 percent) and missed alarm rate (30 percent). The proposed method can provide not only significant improvements for on-site EEW but also a window into the initial P-waves. Studying the trained models might even reveal the hidden indicators inside the initial P-waves.Keywords: earthquake disaster mitigation, on-site type earthquake early warning, machine learning [ABSTRACT FROM AUTHOR]