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Submarine active faults and earthquakes, which contain crucial information to seafloor tectonics and submarine geohazards, can be effectively characterized by precise submarine earthquake catalogs. However, the precise and rapid building of submarine earthquake catalogs is challenging due to the following facts: (i) intense noise in ocean seismic data; (ii) the sparse seismic network; (iii) the lack of historical near-field observations. In this paper, we built a deep-learning-based automatic workflow named ESPRH for automatically building submarine earthquake catalogs from continuous seismograms. The ESPRH workflow integrates Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) for initial earthquake detection and phase picking, PickNet for phase refinement, REAL for earthquake association and rough location, and HypoInverse, HypoDD for precise earthquake relocation. We apply ESPRH to the continuous data recorded by an array of 12 broadband Ocean Bottom Seismographs (OBS) near the Challenger Deep at the southern-most Mariana subduction zone from Dec. 2016 to Jun. 2017. In this study, we acquire a high-resolution local earthquakes catalog that provides new insights into the geometry of shallow fault zones. We report the active submarine faults by seismicity in Challenger Deep which is the deepest place on Earth. These faults are a significant reference for submarine geological hazards and evidence for serpentinization. Hence, the ESPRH is qualified to construct comprehensive local submarine earthquake catalogs automatically, rapidly, and precisely from raw OBS seismic data.

Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep