Premium
Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning
Author(s) -
Li Zefeng,
Meier MenAndrin,
Hauksson Egill,
Zhan Zhongwen,
Andrews Jennifer
Publication year - 2018
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2018gl077870
Subject(s) - seismology , discriminator , geology , waveform , seismic noise , seismometer , warning system , classifier (uml) , noise (video) , extractor , earthquake simulation , computer science , acoustics , artificial intelligence , telecommunications , engineering , radar , physics , detector , process engineering , image (mathematics)
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.