SeisT：一种用于地震监测任务的基础深度学习模型
SeisT: A foundational deep learning model for earthquake monitoring tasks
AI总结：
本研究提出面向多类地震监测任务的基础深度学习模型SeisT，整合多任务定制模块，分布外泛化性能优异，在多项地震监测任务上优于或持平最优模型，其中震相拾取、震级估算提升显著，助力地震信号处理与研究发展。
摘要：
地震图作为基础的地震记录，彻底革新了地震研究与监测工作。近年来深度学习的发展进一步提升了地震信号处理能力，推动地震监测实现更高的精度与效能。本文提出一种基础深度学习模型——地震图Transformer（Seismogram Transformer，简称SeisT），可适配多种地震监测任务。SeisT整合了针对不同任务定制的多个模块，展现出优异的分布外泛化性能，在地震检测、震相拾取、初动极性分类、震级估算、后方位角估算及震中距估算等任务中，性能优于或持平当前最优模型，各项任务的性能得分分别为0.96、0.96、0.68、0.95、0.86、0.55和0.81。与现有模型相比，该模型在P波震相拾取、S波震相拾取和震级估算任务上提升最为显著，提升幅度分别为1.7%、9.5%和8.0%。本研究通过严格的实验与评估表明，SeisT有望推动地震信号处理与地震研究的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
