Earthquake Early Warning ShakeAlert 2.0: Public Rollout

Kohler, Monica D.; Smith, D. E.; Andrews, Jennifer; Chung, A. I.; Hartog, J. Renate; Henson, I. H.; Given, Douglas D.; Groot, Robert D.; Guiwits, Stephen

doi:10.1785/0220190245

Cited by 85 publications

(68 citation statements)

References 26 publications

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“…Here we summarize how GFAST operates in the context of this project; for further details, we refer readers to Crowell et al (2012, 2013) and an operational flowchart in Crowell et al (2016). As an earthquake occurs, GFAST initiates from a seismic trigger, currently from ShakeAlert (Kohler et al, 2020; Murray et al, 2018) that provides the first estimate of the hypocentral location and origin time. This seismic trigger is necessary because GNSS data can have high ambient noise which could otherwise lead to false event triggering during quiescent periods (e.g., Kawamoto et al, 2016; Melgar et al, 2020).…”

Section: Methods and Datamentioning

confidence: 99%

Toward Near‐Field Tsunami Forecasting Along the Cascadia Subduction Zone Using Rapid GNSS Source Models

et al. 2020

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Over the past 15 years and through multiple large and devastating earthquakes, tsunami warning systems have grown considerably in their efficacy in providing timely and accurate forecasts to affected communities. However, one part of tsunami warning that still needs improvement is forecasts catered to local, near‐field communities in the time after an earthquake rupture but before coastal inundation. In this study, we test a rapid, Global Navigation Satellite Systems (GNSS)‐driven earthquake characterization model using a large data set of synthetic megathrust ruptures for its near‐field tsunami forecasting potential. We also provide a framework for tsunami forecasting that focuses on the likelihood of exceedance of user‐defined coastal amplitudes that may be of use in the first 15 min following an earthquake. Specifically, we can estimate the earthquake magnitude, without saturation, for 82% of tested ruptures. We can also identify test ruptures as dominantly thrust events, without analyst guidance for 92% of tested thrust ruptures. Finally, modeling the tsunami component of our rapidly estimated fault rupture leads to greater than 80% accuracy in identifying tsunami impact at key coastal amplitude thresholds. This is promising for near‐field warning when the time prior to inundation is limited to tens of minutes. We focus this study on large megathrust ruptures along the quiescent Cascadia subduction zone where there is already a dense GNSS network.

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Section: Methods and Datamentioning

confidence: 99%

Toward Near‐Field Tsunami Forecasting Along the Cascadia Subduction Zone Using Rapid GNSS Source Models

et al. 2020

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“…The collected seismic data is processed using the United States. West Coast ShakeAlert's Earthquake Pointsource Integrated Code (EPIC) EEW algorithm (Given et al, 2018;Kohler et al, 2020), providing rapid alerts for potentially damaging earthquakes. The EPIC algorithm, formerly under the codename ElarmS (e.g.…”

Section: Truaa Earthquake Early Warning Algorithmmentioning

confidence: 99%

“…As time progress and additional data become available, the origin-time, location, Pd values and magnitude are all updated with higher accuracy (e.g. Kohler et al, 2020). Modifications and developments of ShakeAlert are carefully examined and implemented after adjustments to the local seismic network and conditions (Nof and Allen, 2016;Nof and Kurzon, 2021).…”

Section: Truaa Earthquake Early Warning Algorithmmentioning

confidence: 99%

Earthquake Early Warning System in Israel—Towards an Operational Stage

2021

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The Geological Survey of Israel has upgraded and expanded the national Israeli Seismic Network (ISN), with more than 110 stations country-wide, as part of the implementation of a governmental decision to build a national Earthquake Early Warning (EEW) system named TRUAA. This upgraded seismic network exhibits a high station density and fast telemetry. The stations are distributed mainly along the main fault systems, the Dead Sea Transform, and the Carmel-Zfira Fault, which may potentially produce Mw 7.5 earthquakes. The system has recently entered a limited operational phase, allowing for initial performance estimation. Real-time performance during eight months of operation (41 earthquakes) matches expectations. Alert delays (interval between origin-time and Earthquake Early Warning alert time) are reduced to as low as 3 s, and source parameter errorstatistics are within expected values found in previous works using historical data playbacks. An evolutionary alert policy is implemented based on a magnitude threshold of Mw 4.2 and peak ground accelerations exceeding 2 cm/s2. A comparison between different ground motion prediction equations (GMPE) is presented for earthquakes from Israel and California using median ground motion prediction equations values. This analysis shows that a theoretical GMPE produced the best agreement with observed ground motions, with less bias and lower uncertainties. The performance of this GMPE was found to improve when an earthquake specific stress drop is implemented.

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“…In 2018, the ShakeAlert® Earthquake Early Warning system (ShakeAlert System, ShakeAlert) began the rollout of public EEW alerts for the western states of the U.S. (California, Oregon, and Washington). The goal of the ShakeAlert System is to issue advance warnings to people who may experience potentially damaging shaking so they can take protective actions (Given et al, 2018;Hartog et al, 2016;Kohler et al, 2018;Thakoor et al, 2019;Kohler et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the operational ShakeAlert System includes two independent algorithms (EPIC and FinDer) and plans are in place to investigate if integration of additional algorithms would be beneficial (Given et al, 2018;Kohler et al, 2020). EEW alerts via ShakeAlert (called "ShakeAlert…”

Section: Introductionmentioning

confidence: 99%

The PLUM Earthquake Early Warning Algorithm: A Retrospective Case Study of West Coast, USA, Data

et al. 2021

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The PLUM (Propagation of Local Undamped Motion) earthquake early warning (EEW) algorithm differs from typical source-based EEW algorithms as it predicts shaking directly from observed shaking without first deriving earthquake source information (e.g., magnitude and epicenter).Here, we determine optimal PLUM event detection thresholds for U.S. West Coast earthquakes using two datasets: 558 M3.5+ earthquakes (California, Oregon, Washington;-2017 and the ShakeAlert test suite of historic and problematic signals (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). PLUM computes Modified Mercalli Intensity (IMMI) using velocity and acceleration data, leveraging co-located sensors to avoid problematic signals. An event detection is issued when the observed IMMI exceeds a given threshold(s). We find a two-station detection method using IMMI trigger thresholds of 4.0 and 3.0 for the first and second stations, respectively, is optimal for detecting M4.5+ earthquakes. PLUM detected 79 events in the 2012-2017 dataset, reporting (not including telemetry or alert dissemination) detection times on par, and sometimes faster than current EEW methods (mean 8s; median 6s). As expected, detection times were slower for the older 1999-2015 earthquakes (N=21; mean 11s; median 6s) when station coverage was sparser. Of the 31 PLUM detected M5+ events (10 2012-2017; 21 1999-2015), theoretically 20 (~65%) could provide timely warnings. PLUM issued no false detections and avoided issuing detections for all calibration/anomalous signals, regional and teleseismic events. We conclude PLUM can successfully identify IMMI 4+ shaking from local earthquakes and could complement and enhance EEW in the U.S.

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Earthquake Early Warning ShakeAlert 2.0: Public Rollout

Cited by 85 publications

References 26 publications

Toward Near‐Field Tsunami Forecasting Along the Cascadia Subduction Zone Using Rapid GNSS Source Models

Toward Near‐Field Tsunami Forecasting Along the Cascadia Subduction Zone Using Rapid GNSS Source Models

Earthquake Early Warning System in Israel—Towards an Operational Stage

The PLUM Earthquake Early Warning Algorithm: A Retrospective Case Study of West Coast, USA, Data

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