  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 regions, this method showed superior performance
over previous deep learning-based phase-picking and localization methods.
Overall, our study provides for the first time a prototype self-consistent
all-in-one system of simultaneous seismic phase picking, association, and
location, which has the potential for next-generation autonomous earthquake
monitoring.
