Apparent earthquake rupture predictability

Meier, Men‐Andrin; Ampuero, Jean‐Paul; Cochran, E. S.; Page, Morgan T.

doi:10.1093/gji/ggaa610

Cited by 13 publications

(12 citation statements)

References 32 publications

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“…As our STF model has access to the acceleration parameter investigated in this study, we would expect to be able to reproduce this effect. However, later analysis demonstrated that these results were caused by a sampling bias, which our study confirms (Meier et al, 2020). Danré et al (2019) analysed STFs as well, in this case by decomposing them into subevents, and also found predictability.…”

Section: Comparison With Previous Resultssupporting

confidence: 74%

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A probabilistic view on rupture predictability: all earthquakes evolve similarly

Münchmeyer,

Leser,

Tilmann

2022

Preprint

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Ruptures of the largest earthquakes can last between a few seconds and several minutes. An early assessment of the final earthquake size is essential for early warning systems. However, it is still unclear when in the rupture history this final size can be predicted. Here we introduce a probabilistic view of rupture evolution -how likely is the event to become large -allowing for a clear and wellfounded answer with implications for earthquake physics and early warning. We apply our approach to real time magnitude estimation based on either moment rate functions or broadband teleseismic P arrivals. In both cases, we find strong and principled evidence against early rupture predictability because differentiation between differently sized ruptures only occurs once half of the rupture has been observed. Even then, it is impossible to foresee future asperities. Our results hint towards a universal initiation behavior for small and large ruptures.

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Section: Comparison With Previous Resultssupporting

confidence: 74%

“…This introduces a systematic bias in the first seconds Vallée & Douet (2016). This bias has also been analyzed quantitatively in prior publications Meier et al (2020).…”

Section: A Apparent Early Predictability In Scardecmentioning

confidence: 79%

A probabilistic view on rupture predictability: all earthquakes evolve similarly

Münchmeyer,

Leser,

Tilmann

2022

Preprint

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“…This would lead to magnitude underestimation because of the magnitudes calculated from the newly triggered stations (Melgar and Hayes, 2019;Trugman et al, 2019;Chung et al, 2020) using a short P-wave time window (less than 2 s), especially for the MEMS-based stations ∼50 km away from the epicenter (Table 3). Usually, for a large earthquake with rupture duration longer than 4 s, the magnitude calculated from such a short window will be significantly underestimated (Meier et al, 2016(Meier et al, , 2021Trugman et al, 2019). To avoid this risk, we can use the time window length of each triggered station as a weight for computing the network magnitude.…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of an Earthquake Early Warning System in the 2019–2020 M6.0 Changning, Sichuan, China, Seismic Sequence

Peng

Jiang

et al. 2021

Front. Earth Sci.

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China is currently building a nationwide earthquake early warning system (EEWS) which will be completed in June 2023. Several regions have been selected as pilot areas for instrumentation, software system and dissemination verification. For these regions, their construction tasks will be completed in advance with trial runs being carried out in June 2021. Before the trial operation, we need to understand the actual processing capabilities of different EEWSs. In this work, we focus on the system deployed in Sichuan province and evaluate its real-time performance during the 2019–2020 M6.0 Changning seismic sequence. This period was divided into two stages. The first stage was the time from the occurrence of the M6.0 (Mw5.7) mainshock (June 17, 2019) to the end of October 2019 with no MEMS-based stations around the Changning seismic sequence deployed and most of the broadband and short period seismic stations not upgraded to low latency streaming, and the second one was from the beginning of November 2019 to March 2021 with deployments of more than 700 MEMS-based stations and low latency upgrades of ∼30 seismic stations. Median errors for the epicentral locations, depths and magnitude estimations were almost the same for both stages, 1.5 ± 6.0 km, 0.0 ± 3.6 km and −0.1 ± 0.46 for the first stage and 2.3 ± 3.0 km, −3.0 ± 3.6 km and −0.2 ± 0.32 for the second one. However, an obvious underestimation of the magnitude for earthquakes with M 5.0 + occurring in the first stage was observed, which would be caused by the clipped waveforms, sensors deployed in short period seismic stations and MEMS-based stations, the adopted magnitude estimation method, and the method used to computer the network magnitude. The median reporting time was significantly improved from 10.5 ± 3.0 s after origin time for the first stage to 6.3 ± 3.5 s for the second stage because of introduction of newly deployed MEMS-based stations. The results obtained from the second stage indicate that the system has entered a stable operating stage and we can officially launch the trial operation in the pilot areas for public early warning services.

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“…Then the temporal evolution of the observed and predicted intensity envelope updates as the FinDer magnitude and orientation of line source changes. When applying the lower alert threshold and higher damage threshold, a potential expected consequence is to yield better classification as suggested by previous tentative and observational studies Ruhl et al 2019;Meier et al 2020;Chung et al 2020). To evaluate the influence of alert criterion exerting on the classification performance during the Ridgecrest earthquake sequence, we provide six choices of alert criterion to explore in which circumstance FinDer algorithm can achieve the best alert performance including classification.…”

Section: The Simulated Real Time Alert Performancementioning

confidence: 99%

Retrospective Study of FinDer Algorithm During the 2019, Ridgecrest Earthquake Sequence

Huang¹

2021

Preprint

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Real-time characterization of evolving rupture is crucial for mitigating against seismic hazards exposed to potentially devastating earthquake events in EEWs (Earthquake Early Warning system). Currently, FinDer (Finite Fault Rupture Detector) algorithm explicitly utilizes observed ground motion pattern to solve for the evolving rupture to generate alerts for early warning purpose, which is currently contributing to ShakeAlert EEW system in West Coast of United States, within the area covered by the Advanced National Seismic System (ANSS) network. Here we implement FinDer offline to explore its feasibility assuming ideal field telemetry on a database of real earthquakes with magnitude M ≥5.0 occurring in Ridgecrest, Southern California in 2019. We specially focus on evaluating the performance of FinDer through end-user-orientated analysis in terms of warning time and accuracy of ground shaking prediction. Overall, FinDer classifies alerts with a rate of success over 74% across a broad range of alert criteria, substantial fraction of sites can be successfully alerted including the most difficult cases with high ground motion intensities regardless of invariable few seconds of warning time. FinDer can be configured to generate more useful alerts with higher cost savings by applying lower alert threshold during the Ridgecrest earthquake sequence. Furthermore, although large fractions of sites would have been timely alerted, it is significantly challenging for predicting accurately the moderate or worse intensities (Modified Mercalli Intensity > 5.5) in advance even if applying lower alert threshold and higher damage threshold. Nonetheless, FinDer performs well in an evolutionary manner to guarantee reliable alerts by resorting to a consistent description of point source or occurring rupture.

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Apparent earthquake rupture predictability

Cited by 13 publications

References 32 publications

A probabilistic view on rupture predictability: all earthquakes evolve similarly

A probabilistic view on rupture predictability: all earthquakes evolve similarly

Performance Evaluation of an Earthquake Early Warning System in the 2019–2020 M6.0 Changning, Sichuan, China, Seismic Sequence

Retrospective Study of FinDer Algorithm During the 2019, Ridgecrest Earthquake Sequence

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