Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

Zefeng Li; Men‐Andrin Meier; Egill Hauksson; Zhongwen Zhan; Jennifer Andrews

doi:10.1029/2018gl077870

journal article May 28, 2018

Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

Zefeng Li

Geophysical Research Letters Vol. 45 No. 10 pp. 4773-4779 · American Geophysical Union (AGU)

View at Publisher Save 10.1029/2018gl077870

Abstract

AbstractPerformance 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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Metrics

273

Citations

17

References

Details

Published: May 28, 2018
Vol/Issue: 45(10)
Pages: 4773-4779
License: View

Authors

Z

Zefeng Li