A Caltech Library Service

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. doi:10.1029/2018GL077870.

[img] PDF - Published Version
See Usage Policy.

[img] MS Word - Supplemental Material
See Usage Policy.


Use this Persistent URL to link to this item:


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
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 (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 ( and Scikit‐learn (http://scikit‐ 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
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:
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.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:86386
Deposited By: Tony Diaz
Deposited On:14 May 2018 16:20
Last Modified:15 Nov 2021 20:38

Repository Staff Only: item control page