Earthquake Early Warning ShakeAlert System: West Coast Wide Production Prototype

Kohler, Monica D.; Cochran, E. S.; Given, Douglas D.; Guiwits, Steve; Neuhauser, D. S.; Henson, I. H.; Hartog, J. Renate; Bodin, Paul; Kress, V. C.; Thompson, Stephen L.; Felizardo, C.; Brody, Jeff; Bhadha, R.; Schwarz, Stan

doi:10.1785/0220170140

Cited by 88 publications

(62 citation statements)

References 37 publications

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“…EEW uses techniques that are different from traditional earthquake detection and location methods to estimate earthquake source information and determine expected intensity of ground shaking across a region (see Given et al, 2014;Kohler et al, 2017). Because the goal of the ShakeAlert system is to provide information about expected ground shaking before it arrives at an end user's location, the tests to verify system and algorithm performance must take into account latency of alert information as well as the accuracy of point-source information and predicted ground shaking.…”

Section: Data and Testing Proceduresmentioning

confidence: 99%

“…The real-time test implements a system and software environment that replicates the production system, except for the inclusion of the new or revised candidate code. Through the real-time in situ tests, operational problems can be identified before implementation on production servers (for details, see Kohler et al, 2017). The real-time test servers are collocated with the production servers in U.S. Geological Survey (USGS)-Pasadena/California Institute of Technology, USGS-Menlo Park, University of California at Berkeley, and the University of Washington.…”

Section: Real-time In Situ Test Serversmentioning

confidence: 99%

“…Following nearly 10 years of research and development, a Production Prototype ShakeAlert system was released in California in 2016, and across the West Coast of the United States (California, Oregon, and Washington states) in 2017 (see Kohler et al, 2017). Alerts are currently being used by a selected group of community participants, but as the system is developed, the number and types of end users will expand.…”

Section: Introduction: Purpose and Contextmentioning

confidence: 99%

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Earthquake Early Warning ShakeAlert System: Testing and Certification Platform

Cochran

Kohler

Given

et al. 2017

Seismological Research Letters

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Earthquake early warning systems provide warnings to end users of incoming moderate to strong ground shaking from earthquakes. An earthquake early warning system, ShakeAlert, is providing alerts to beta end users in the western United States, specifically California, Oregon, and Washington. An essential aspect of the earthquake early warning system is the development of a framework to test modifications to code to ensure functionality and assess performance. In 2016, a Testing and Certification Platform (TCP) was included in the development of the Production Prototype version of ShakeAlert. The purpose of the TCP is to evaluate the robustness of candidate code that is proposed for deployment on ShakeAlert Production Prototype servers. TCP consists of two main components: a real-time in situ test that replicates the real-time production system and an offline playback system to replay test suites. The real-time tests of system performance assess code optimization and stability. The offline tests comprise a stress test of candidate code to assess if the code is production ready. The test suite includes over 120 events including local, regional, and teleseismic historic earthquakes, recentering and calibration events, and other anomalous and potentially problematic signals. Two assessments of alert performance are conducted. First, point-source assessments are undertaken to compare magnitude, epicentral location, and origin time with the Advanced National Seismic System Comprehensive Catalog, as well as to evaluate alert latency. Second, we describe assessment of the quality of ground-motion predictions at end-user sites by comparing predicted shaking intensities to ShakeMaps for historic events and implement a threshold-based approach that assesses how often end users initiate the appropriate action, based on their ground-shaking threshold. TCP has been developed to be a convenient streamlined procedure for objectively testing algorithms, and it has been designed with flexibility to accommodate significant changes in development of new or modified system code. It is expected that the TCP will continue to evolve along with the ShakeAlert system, and the framework we describe here provides one example of how earthquake early warning systems can be evaluated.Electronic Supplement: Tables of test suite events used to assess the ShakeAlert system and a thorough description of the nondeterministic behavior of the system during test runs.

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Section: Data and Testing Proceduresmentioning

confidence: 99%

Section: Real-time In Situ Test Serversmentioning

confidence: 99%

Section: Introduction: Purpose and Contextmentioning

confidence: 99%

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Earthquake Early Warning ShakeAlert System: Testing and Certification Platform

Cochran

Kohler

Given

et al. 2017

Seismological Research Letters

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“…The noise data are waveforms that triggered the short-term average/long-term average filter of the real-time ShakeAlert EEW system (Kohler et al, 2017) from January 2015 to April 2017 across the SCSN. We remove all triggers within 2 min of any local or regional earthquakes, as well as quarry blasts in the SCEDC catalog, to avoid routinely determined earthquakes and blasts in the noise data set.…”

Section: Datamentioning

confidence: 99%

Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

Meier

Hauksson

et al. 2018

Geophysical Research Letters

251

105

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Performance of earthquake early warning systems suffers from false alerts caused by local impulsive noise from natural or anthropogenic sources. To mitigate this problem, we train a generative adversarial network (GAN) to learn the characteristics of first‐arrival earthquake P waves, using 300,000 waveforms recorded in southern California and Japan. We apply the GAN critic as an automatic feature extractor and train a Random Forest classifier with about 700,000 earthquake and noise waveforms. We show that the discriminator can recognize 99.2% of the earthquake P waves and 98.4% of the noise signals. This state‐of‐the‐art performance is expected to reduce significantly the number of false triggers from local impulsive noise. Our study demonstrates that GANs can discover a compact and effective representation of seismic waves, which has the potential for wide applications in seismology.

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“…The ShakeAlert EEW system for the U.S. West Coast (Given et al, ; Kohler et al, ) requires detections on at least four sites to issue an alert. Despite this requirement, false event declarations occur several times per year (Cochran et al, ).…”

Section: Introductionmentioning

confidence: 99%

Reliable Real‐Time Seismic Signal/Noise Discrimination With Machine Learning

et al. 2019

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In earthquake early warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental-and difficult-tasks in EEW is to rapidly and reliably discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on very little information, typically a few seconds worth of seismic waveforms from a small number of stations. As a result, current EEW systems struggle to avoid discrimination errors and suffer from false and missed alerts. In this study we show how modern machine learning classifiers can strongly improve real-time signal/noise discrimination. We develop and compare a series of nonlinear classifiers with variable architecture depths, including fully connected, convolutional and recurrent neural networks, and a model that combines a generative adversarial network with a random forest. We train all classifiers on the same data set, which includes 374 k local earthquake records (M3.0-9.1) and 946 k impulsive noise signals. We find that all classifiers outperform existing simple linear classifiers and that complex models trained directly on the raw signals yield the greatest degree of improvement. Using 3-s-long waveform snippets, the convolutional neural network and the generative adversarial network with a random forest classifiers both reach 99.5% precision and 99.3% recall on an independent validation data set. Most misclassifications stem from impulsive teleseismic records, and from incorrectly labeled records in the data set. Our results suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts.Plain Language Summary Seismic stations record not only earthquake signals but also a wide variety of nuisance signals. Some of these nuisance signals are impulsive and can initially look very similar to real earthquake signals. This is a problem for earthquake early warning (EEW) algorithms, which sometimes misinterpret such signals as being real earthquake signals, and which may then send out false alerts. For each registered impulsive signal, EEW systems need to decide (or, classify) in real-time whether or not the signal stems from an actual earthquake. State-of-the-art machine learning (ML) classifiers have been shown to strongly outperform more standard linear classifiers in a wide range of classification problems. Here we analyze the performance of a variety of different ML classifiers to identify which type of classifier leads to the most reliable signal/noise discrimination in an EEW context. We find that we can successfully train complex deep learning classifiers that can discriminate between nuisance and earthquake signals very reliably (accuracy of 99.5%). Less complex ML classifiers also outperform a linear classifier, but with significantly higher error rates. The deep ML classifiers may allow EEW systems to almost entirely avoid false and missed signal detections...

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Earthquake Early Warning ShakeAlert System: West Coast Wide Production Prototype

Cited by 88 publications

References 37 publications

Earthquake Early Warning ShakeAlert System: Testing and Certification Platform

Earthquake Early Warning ShakeAlert System: Testing and Certification Platform

Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

Reliable Real‐Time Seismic Signal/Noise Discrimination With Machine Learning

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