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

摘要

Earthquake 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 similar to 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.