Multi-events earthquake early warning algorithm using a Bayesian approach

Wu, Stephen; Yamada, Masumi; Tamaribuchi, Koji; Beck, James L.

doi:10.1093/gji/ggu437

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…In their method for identification of multiple events for EEWS, Liu and Yamada (2014) and Wu et al. (2015) use both P‐ and S‐wave travel times and amplitudes to constrain the earthquake location and magnitude of events occurring in an aftershock sequence. In particular, Wu et al.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Method for Real‐Time Earthquake Location Using Multiparameter Data

et al. 2021

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A primary task of a network‐based, earthquake early warning system is the prompt event detection and location, needed to assess the magnitude of the event and its potential damage through the predicted peak ground shaking amplitude using empirical attenuation relationships. Most of real‐time, automatic earthquake location methods ground on the progressive measurement of the first P‐wave arrival time at stations located at increasing distances from the source but recent approaches showed the feasibility to improve the accuracy and rapidity of the earthquake location by using the additional information carried by the P‐wave polarization or amplitude, especially with unfavorable seismic network lay‐outs. Here, we propose an evolutionary, Bayesian method for the real‐time earthquake location which combines the information derived from the differential P‐wave arrival times, amplitude ratios and back‐azimuths measured at a minimum of two stations. As more distant stations record the P‐wave, the posterior pdf is updated and new earthquake location parameters are determined along with their uncertainty. To validate the location method, we performed a retrospective analysis of mainshocks (M > 4.5) occurred during the 2016–2017 Central Italy earthquake sequence by simulating the typical acquisition layouts of in‐land, coastal and linear array of stations. Results show that with the combined use of the three parameters, 2–4 s after the first P‐wave detection, the method converges to stable and accurate determinations of epicentral coordinates and depth even with a nonoptimal coverage of stations. The proposed methodology can be generalized and adapted to the off‐line analysis of seismic records collected by standard local networks.

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Section: Introductionmentioning

confidence: 99%

“…In particular, Wu et al. (2015), propose a Bayesian, EEW probabilistic scheme to identify multiple concurrent earthquakes through the scan of a posterior probability density function which jointly uses P‐wave time and displacement amplitude information from triggered and not‐triggered stations.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Method for Real‐Time Earthquake Location Using Multiparameter Data

et al. 2021

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“…Therefore, the proposed approaches would still work even if an aftershock strikes. For more consideration of aftershocks, the readers can refer to the paper of Wu et al., ().…”

Section: Resultsmentioning

confidence: 99%

Two Novel Approaches to Reduce False Alarm Due to Non‐Earthquake Events for On‐Site Earthquake Early Warning System

Hsu

Chang

2016

Computer aided Civil Eng

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An on‐site earthquake early warning system (EEWS) can provide more lead‐time at regions that are close to the epicenter of an earthquake because only seismic information of a target site is required. Instead of leveraging the information of several stations, the on‐site system extracts some P‐wave features from the first few seconds of vertical ground acceleration of a single station. It then predicts the intensity of the forthcoming earthquake at the same station according to these features. However, the system may be triggered by some vibration signals that are not caused by an earthquake or by interference from electronic signals, which may consequently result in a false alarm at the station. Thus, this study proposes two approaches to distinguish the vibration signals caused by non‐earthquake events from those caused by earthquake events based on support vector classification (SVC) and singular spectrum analysis (SSA). In the first approach (Approach I), the fast Fourier transform algorithm and the established SVC model are employed to classify the vibration signals. In the second approach (Approach II), a SSA criterion is added to Approach I for the purpose of identifying earthquake events that are classified as non‐earthquake events by the SVC model with increased accuracy. Both approaches are verified by using data collected from the Taiwan Strong Motion Instrumentation Program and EEW stations of the National Center for Research on Earthquake Engineering. The results indicate that both of the proposed approaches effectively reduce the possibility of false alarms caused by an unknown vibration event.

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“…convincingly demonstrated how a well-selected set of logic criteria can drastically reduce misclassifications (e.g., false alerts). Presumably, even stronger performance could be obtained if the classification was based on the frequency content of observed real-time waveforms (e.g., Beyreuther and Wassermann, 2011;Hammer et al, 2012;Wu et al, 2015). Furthermore, in its current formulation, the GBA is limited to areas with predominantly shallow crustal seismicity.…”

Section: Discussionmentioning

confidence: 99%

The Gutenberg Algorithm: Evolutionary Bayesian Magnitude Estimates for Earthquake Early Warning with a Filter Bank

Meier¹,

Heaton²,

Clinton³

2015

Bulletin of the Seismological Society of America

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Earthquake early warning (EEW) is a race against time. In particular, at proximal sites to the epicenter (typically the most heavily affected sites), strong ground motion starts shortly after the P-wave onset. For these sites, regional-type EEW systems that wait until data from several stations are available before issuing a warning and that require fixed data windows following a trigger are not fast enough. Single-station algorithms, on the other hand, have high uncertainties that compromise their usefulness. In this article, we propose that uncertainties of the earliest warning messages can be reduced substantially if the broadband frequency information of seismic signals is fully exploited. We present a novel probabilistic algorithm for estimating EEW magnitudes. The Gutenberg algorithm uses a filter bank for a time-frequency analysis of the realtime signals and estimates the posterior probabilities of both magnitude and sourcestation distance directly from the observed frequency content. It starts off as a singlestation algorithm and then naturally evolves into a regional-type algorithm, as more data become available. Using an extensive near-source waveform data set, we demonstrate that the Gutenberg parameter estimates reach the estimation accuracy and precision of existing regional-type EEW systems with only 3 s of data from a single station. The magnitude estimates, however, saturate at a threshold magnitude that depends on the available signal length that is used for the estimation, suggesting that current EEW magnitude estimates (1) are observational rather than predictive and (2) have to be considered minimum estimates, depending on the amount of available data.

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Multi-events earthquake early warning algorithm using a Bayesian approach

Cited by 16 publications

References 15 publications

A Bayesian Method for Real‐Time Earthquake Location Using Multiparameter Data

A Bayesian Method for Real‐Time Earthquake Location Using Multiparameter Data

Two Novel Approaches to Reduce False Alarm Due to Non‐Earthquake Events for On‐Site Earthquake Early Warning System

The Gutenberg Algorithm: Evolutionary Bayesian Magnitude Estimates for Earthquake Early Warning with a Filter Bank

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