The Propagation of Local Undamped Motion (PLUM) Method: A Simple and Robust Seismic Wavefield Estimation Approach for Earthquake Early Warning

Kodera, Yuki; Yamada, Yasuyuki; Hirano, Kazuyuki; Tamaribuchi, Koji; Adachi, Shimpei; Hayashimoto, Naoki; Morimoto, Masahiko; Nakamura, Masaki; Hoshiba, Mitsuyuki

doi:10.1785/0120170085

Cited by 73 publications

(80 citation statements)

References 41 publications

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“…The ShakeAlert system (Given et al, 2018) computes polygons within which the expected ground motion is above an alerting level. The operational JMA EEW system (Kodera et al, 2018) alerts 188 "forecast regions" (Doi, 2011). If any of the numerous alerting points inside a forecast region exceeds the alerting threshold, the entire region is alerted, including the parts of the region that are located at larger distances.…”

Section: Discussionmentioning

confidence: 99%

“…Here the PLUM and FinDer algorithms have a key advantage over standard point-source algorithms in that they do not strongly rely on phase associations. PLUM's ground motion predictions can be made entirely independent of event associations (Kodera et al, 2018). Similarly, FinDer performs a continuous ground motion association, by finding the best single model that best explains the observed ground motion field, independent of pick-associations.…”

Section: Discussionmentioning

confidence: 99%

“…The PLUM method (Kodera et al, 2018) is a simplified version of the wavefield-based method proposed by Hoshiba (2013) and Hoshiba and Aoki (2015). It predicts shaking intensities at all sites in a network without characterizing the seismic source, but by extrapolating the observed shaking intensities at nearby sites.…”

Section: The Plum Algorithmmentioning

confidence: 99%

“…(2) The Finite-Fault Rupture Detector (FinDer) algorithm (Böse et al, 2018) uses precomputed binary ground motion templates to identify and track the line source that best explains the observed high frequency ground motion; it is the seismic finite-source algorithm in ShakeAlert. (3) The Propagation of Local Undamped Motion (PLUM) algorithm (Kodera et al, 2018) uses ground motion amplitude observations at nearby sites to predict impending ground motion at target sites without characterizing the earthquake source; it is one of the two operational algorithms in the Japanese public EEW system operated by the Japan Meteorological Agency (JMA, Doi, 2011;Kodera et al, 2018). The three algorithms are arguably among the most promising EEW algorithms, and they represent three different approaches to EEW.…”

Section: Introductionmentioning

confidence: 99%

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How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion?

et al. 2020

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Although numerous Earthquake Early Warning (EEW) algorithms have been developed to date, we lack a detailed understanding of how often and under what circumstances useful ground motion alerts can be provided to end users. In particular, it is unclear how often EEW systems can successfully alert sites with high ground motion intensities. These are the sites that arguably need EEW alerts the most, but they are also the most challenging ones to alert because they tend to be located close to the epicenter where the seismic waves arrive first. Here we analyze the alerting performance of the Propagation of Local Undamped Motion (PLUM), Earthquake Point‐Source Integrated Code (EPIC), and Finite‐Fault Rupture Detector (FinDer) algorithms by running them retrospectively on the seismic strong‐motion data of the 219 earthquakes in Japan since 1996 that exceeded Modified Mercalli Intensity (MMI) of 4.5 on at least 10 sites (Mw 4.5–9.1). Our analysis suggests that, irrespective of the algorithm, EEW end users should expect that EEW can often but not always provide useful alerts. Using a conservative warning time (tw) definition, we find that 40–60% of sites with strong to extreme shaking levels receive alerts with tw > 5 s. If high‐intensity shaking is caused by shallow crustal events, around 50% of sites with strong (MMI~6) and <20% of sites with severe and violent (MMI ≥ 8) shaking receive alerts with tw > 5 s. Our results provide detailed quantitative insight into the expected alerting performance for EEW algorithms under realistic conditions. We also discuss how operational systems can achieve longer warning times with more precautionary alerting strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: The Plum Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion?

et al. 2020

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“…In fact, observed intensities of ground motions are not necessarily in agreement with the estimates by a big-net EEW. Recently, researchers proposed a new method of "PLUM" (Propagation of Local Undamped Motion) for the JMA EEW system to better predict seismic intensities directly from the observed real-time intensities near target sites (Kodera et al 2018).…”

Section: Variation Of Ground Motions and Site-specific Factorsmentioning

confidence: 99%

Earthquake early warning: what does “seconds before a strong hit” mean?

Tajima

Hayashida

2018

Prog Earth Planet Sci

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An earthquake early warning (EEW) system is designed to detect an event, determine its parameters (hypocenter, magnitude, and origin time), and issue an alert to sites/areas where necessary actions should be taken before destructive seismic energy arrivals. At present, large-scale EEW systems are operational in several countries around the world. The most extensive nationwide EEW system has been operating in Japan since 2007, and was able to issue alerts broadly when the moment magnitude (Mw) 9 Tohoku-Oki earthquake hit in 2011. The casualties caused by this event were far less than those caused by other deadly earthquakes (Mw > 6.6) in this century. Many other countries attributed the fewer death victims to the advanced large-scale EEW system, and plan to install systems similar to Japan's model. However, the historical and environmental background in Japan, both in terms of earthquake hazards and safety preparation, differs considerably from other countries. In addition, EEW systems that use data from a large-scale network (i.e., "a big-net" hereafter) still have limitations. There are thus numerous factors that other countries should consider to benefit from installing a Japan-styled EEW. In this article, we review how research and development associated with EEW have been carried out, and how EEW systems presently function. We then show shortwavelength variation of ground motions within the typical station interval of a big-net using data recorded by a dense local seismic network in Japan. However, it is not particularly meaningful to attempt detailed modeling of varieties of ground motion within the station interval for a big-net EEW operation, because the possible combinations of earthquake sources, paths of wave propagation, and recipient sites are infinite. We emphasize that in all circumstances, for recipients to benefit from EEW, seismic safety preparations must be implemented. Necessary preparations at sites do not diminish in importance after incremental improvements in station coverage and/or algorithms in a big-net operation. Further, scientists and engineers involved in EEW projects should strive to publically disseminate how big-net EEW systems work, and also why, to achieve maximum benefit, these systems should always be supplemented by preparations at recipients' sites.

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Early Peak Ground Acceleration Prediction for On-Site Earthquake Early Warning Using LSTM Neural Network

Hsu

Pratomo

2021

Preprint

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The Propagation of Local Undamped Motion (PLUM) Method: A Simple and Robust Seismic Wavefield Estimation Approach for Earthquake Early Warning

Cited by 73 publications

References 41 publications

How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion?

How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion?

Earthquake early warning: what does “seconds before a strong hit” mean?

Early Peak Ground Acceleration Prediction for On-Site Earthquake Early Warning Using LSTM Neural Network

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