  Seismograms, the fundamental seismic records, have revolutionized earthquake
research and monitoring. Recent advancements in deep learning have further
enhanced seismic signal processing, leading to even more precise and effective
earthquake monitoring capabilities. This paper introduces a foundational deep
learning model, the Seismogram Transformer (SeisT), designed for a variety of
earthquake monitoring tasks. SeisT combines multiple modules tailored to
different tasks and exhibits impressive out-of-distribution generalization
performance, outperforming or matching state-of-the-art models in tasks like
earthquake detection, seismic phase picking, first-motion polarity
classification, magnitude estimation, back-azimuth estimation, and epicentral
distance estimation. The performance scores on the tasks are 0.96, 0.96, 0.68,
0.95, 0.86, 0.55, and 0.81, respectively. The most significant improvements, in
comparison to existing models, are observed in phase-P picking, phase-S
picking, and magnitude estimation, with gains of 1.7%, 9.5%, and 8.0%,
respectively. Our study, through rigorous experiments and evaluations, suggests
that SeisT has the potential to contribute to the advancement of seismic signal
processing and earthquake research.
