An all-in-one seismic phase picking, location, and association network for multi-task multi-station earthquake monitoring

Abstract

Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdependent tasks of phase picking, association, and location. Although deep learning methods have been successfully applied to earthquake monitoring, they mostly address the tasks separately and ignore the geographic relationships among stations. Here, we propose a graph neural network that operates directly on multi-station seismic data and achieves simultaneous phase picking, association, and location. Particularly, the inter-station and inter-task physical relationships are informed in the network architecture to promote accuracy, interpretability, and physical consistency among cross-station and cross-task predictions. When applied to data from the Ridgecrest region and Japan, this method showed superior performance over previous deep learning-based phase-picking and localization methods. Overall, our study provides a prototype self-consistent all-in-one system of simultaneous seismic phase picking, association, and location, which has the potential for next-generation automated earthquake monitoring.