Seismic records, known as seismograms, are crucial records of ground motion
resulting from seismic events, constituting the backbone of earthquake research
and monitoring. The latest advancements in deep learning have significantly
facilitated various seismic signal processing tasks. This paper introduces a
novel backbone neural network model designed for various seismic monitoring
tasks, named Seismogram Transformer (SeisT). Thanks to its efficient network
architecture, SeisT matches or even outperforms the state-of-the-art models in
earthquake detection, seismic phase picking, first-motion polarity
classification, magnitude estimation, and azimuth estimation tasks,
particularly in terms of out-of-distribution generalization performance. SeisT
consists of multiple network layers composed of different foundational blocks,
which help the model understand multi-level feature representations of
seismograms from low-level to high-level complex features, effectively
extracting features such as frequency, phase, and time-frequency relationships
from input seismograms. Three different-sized models were customized based on
these diverse foundational modules. Through extensive experiments and
performance evaluations, this study showcases the capabilities and potential of
SeisT in advancing seismic signal processing and earthquake research.

该研究介绍了一种名为 Seismogram Transformer（SeisT）的新型骨干神经网络模型，用于各种地震监测任务，包括地震检测、相位拾取、正负首动分类、震级估计和方位估计。通过对输入地震图的多层级特征表示的理解，该模型能够从低级到高级复杂特征有效地提取频率、相位和时频关系等特征，展示了 SeisT 在推进地震信号处理和地震研究方面的能力和潜力。