Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

DOI number:10.1029/2018gl077870

Affiliation of Author(s):中国科学技术大学地球和空间科学学院

Journal:Geophysical Research Letters

Place of Publication:美国

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 si

First Author:Zefeng Li

Co-author:Men-Andrin Meier, Egill Hauksson, Zhongwen Zhan, Jennifer Andrews

Indexed by:Journal paper

Correspondence Author:Zefeng Li

Document Code:9

Discipline:Natural Science

First-Level Discipline:Geophysics

Document Type:SCI

Volume:45

Issue:10

Page Number:4773-4779

ISSN No.:0094-8276

Translation or Not:no

Date of Publication:2018-05-29

Links to published journals:https://doi.org/10.1029/2018gl077870