Siamese Earthquake Transformer: A Pair‐Input Deep‐Learning Model for Earthquake Detection and Phase Picking on a Seismic Array

Abstract

AbstractEarthquake detection and phase picking play a fundamental role in studying seismic hazards and the Earth’s interior. Many deep‐learning‐based methods, including the state‐of‐the‐art model called Earthquake Transformer (EqT), have made considerable progress. However, the processing of low signal‐to‐noise ratio (SNR) seismograms remains a challenge. Here, we present a pair‐input deep‐learning model called Siamese Earthquake Transformer (S‐EqT), which achieves good performance on low SNR seismograms using the latent information in the deep‐learning black box of the pre‐trained EqT model on a seismic array. We compare the EqT and S‐EqT models on 2 weeks of continuous seismograms recorded by stations around northern Los Angeles region in California. In addition to showing a good performance similar to the EqT model on high SNR seismograms, the S‐EqT model retrieves ∼40% more reliable picks from low SNR seismograms, resulting in better earthquake characterizations. Our method provides a novel perspective on earthquake monitoring by highlighting the importance of hidden responses inside a deep‐learning model and shows its great potential for seismology.