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Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

Li, Zefeng and Meier, Men-Andrin and Hauksson, Egill and Zhan, Zhongwen and Andrews, Jennifer (2018) Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning. Geophysical Research Letters, 45 (10). pp. 4773-4779. ISSN 0094-8276. https://resolver.caltech.edu/CaltechAUTHORS:20180514-084022982

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1029/2018GL077870DOIArticle
ORCID:
AuthorORCID
Li, Zefeng0000-0003-4405-8872
Meier, Men-Andrin0000-0002-2949-8602
Hauksson, Egill0000-0002-6834-5051
Zhan, Zhongwen0000-0002-5586-2607
Andrews, Jennifer0000-0002-5679-5565
Additional Information:© 2017 American Geophysical Union. Received 9 MAR 2018; Accepted 6 MAY 2018; Accepted article online 11 MAY 2018; Published online 29 MAY 2018. We thank Qingkai Kong and another anonymous reviewer for constructive comments. This research was supported by a Gordon and Betty Moore Foundation grant to Caltech and by the Swiss National Science Foundation. The Japanese waveform data can be downloaded from http://www.kik.bosai.go.jp/ (last accessed October 2017). For southern California we have used waveforms and parametric data from the Caltech/USGS Southern California Seismic Network (doi:10.7914/SN/CI) stored at the Southern California Earthquake Data Center (doi:10.7909/C3WD3xH1). The algorithms were written with Python packages Keras (https://keras.io/) and Scikit‐learn (http://scikit‐learn.org/). The GAN was trained for 2.0 hr, and the Random Forest was trained for 20 min on a PC (NVIDIA GeForce GTX 1050 Ti 4 GB, Intel Core i5‐7300HQ 2.50GHz).
Group:Seismological Laboratory
Funders:
Funding AgencyGrant Number
Gordon and Betty Moore FoundationUNSPECIFIED
Swiss National Science Foundation (SNSF)UNSPECIFIED
Subject Keywords:Machine learning; Earthquake early warning; Seismic waves
Issue or Number:10
Record Number:CaltechAUTHORS:20180514-084022982
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180514-084022982
Official Citation:Li, Z., Meier, M.‐A., Hauksson, E., Zhan, Z., & Andrews, J. (2018). Machine learning seismic wave discrimination: Application to earthquake early warning. Geophysical Research Letters, 45, 4773–4779. https://doi.org/10.1029/2018GL077870
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:86386
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 May 2018 16:20
Last Modified:03 Oct 2019 19:43

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