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. doi:10.1029/2018GL077870. 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.
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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 | ||||||||||||
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Subject Keywords: | Machine learning; Earthquake early warning; Seismic waves | ||||||||||||
Issue or Number: | 10 | ||||||||||||
DOI: | 10.1029/2018GL077870 | ||||||||||||
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: | 15 Nov 2021 20:38 |
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