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Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center

Yeck, William Luther and Patton, John M. and Ross, Zachary E. and Hayes, Gavin P. and Guy, Michelle R. and Ambruz, Nick B. and Shelly, David R. and Benz, Harley M. and Earle, Paul S. (2021) Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center. Seismological Research Letters, 92 (1). pp. 469-480. ISSN 0895-0695. doi:10.1785/0220200178.

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Machine‐learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real‐time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep‐learning tools into NEIC operations. Automatic seismic arrival detections made from standard picking methods (e.g., short‐term average/long‐term average [STA/LTA]) are fed to trained neural network models to improve automatic seismic‐arrival (pick) timing and estimate seismic‐arrival phase type and source‐station distances. These additional data are used to improve the capabilities of the NEIC associator. We compile a dataset of 1.3 million seismic‐phase arrivals that represent a globally distributed set of source‐station paths covering a range of phase types, magnitudes, and source distances. We train three separate convolutional neural network models to predict arrival time onset, phase type, and distance. We validate the performance of the trained networks on a subset of our existing dataset and further extend validation by exploring the model performance when applied to NEIC automatic pick data feeds. We show that the information provided by these models can be useful in downstream event processing, specifically in seismic‐phase association, resulting in reduced false associations and improved location estimates.

Item Type:Article
Related URLs:
URLURL TypeDescription
Yeck, William Luther0000-0002-2801-8873
Patton, John M.0000-0003-0142-5118
Ross, Zachary E.0000-0002-6343-8400
Hayes, Gavin P.0000-0003-3323-0112
Guy, Michelle R.0000-0003-3450-4656
Ambruz, Nick B.0000-0002-3660-3546
Shelly, David R.0000-0003-2783-5158
Benz, Harley M.0000-0002-6860-2134
Earle, Paul S.0000-0002-3500-017X
Additional Information:© 2021 Seismological Society of America. Manuscript received 8 May 2020. Published online 23 September 2020. The facilities of Incorporated Research Institutions for Seismology (IRIS) Data Services, and specifically the IRIS Data Management Center, were used for access to waveforms used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience and EarthScope (SAGE) Proposal of the National Science Foundation under Cooperative Agreement EAR‐1261681. Waveform data from 136 network codes were used in this article, and the authors are thankful to the numerous network operators for making this work possible. The authors would like to thank Kyle Withers, two anonymous reviewers, and Associate Editor Allison Bent for their help improving this article.
Group:Seismological Laboratory
Funding AgencyGrant Number
Issue or Number:1
Record Number:CaltechAUTHORS:20210205-145940821
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Official Citation:William Luther Yeck, John M. Patton, Zachary E. Ross, Gavin P. Hayes, Michelle R. Guy, Nick B. Ambruz, David R. Shelly, Harley M. Benz, Paul S. Earle; Leveraging Deep Learning in Global 24/7 Real‐Time Earthquake Monitoring at the National Earthquake Information Center. Seismological Research Letters 2020;; 92 (1): 469–480. doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107938
Deposited By: George Porter
Deposited On:05 Feb 2021 23:35
Last Modified:16 Nov 2021 19:07

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