The template matching method ( Gibbons and Ringdal, 2006 Peng and Zhao, 2009) is widely used for building earthquake catalogs by exploiting the similarity of earthquake waveforms between nearby earthquakes using previously identified earthquake templates.
However, these methods are less precise than human experts and rely on hyperparameters, limiting their performance when processing complex seismic data with different types of noise and variable signal-to-noise ratios. Many traditional automatic earthquake detection methods have been proposed to address this problem, such as short-term average/long-term average algorithm (STA/LTA) ( Allen, 1978), autoregression with Akaike Information Criterion (AIC) ( Sleeman and van Eck, 1999). However, seismic data is generally extensive, and it is subjective and time-consuming to extract earthquake signals by human experts manually. A local earthquake catalog can reveal the detailed geometry of faults, providing critical insights into tectonics and earthquake disasters. Submarine seismicity and active faults are essential for the analysis and monitoring of submarine geohazards. Hence, the ESPRH is qualified to construct comprehensive local submarine earthquake catalogs automatically, rapidly, and precisely from raw OBS seismic data. These faults are a significant reference for submarine geological hazards and evidence for serpentinization. We report the active submarine faults by seismicity in Challenger Deep which is the deepest place on Earth. In this study, we acquire a high-resolution local earthquakes catalog that provides new insights into the geometry of shallow fault zones. 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. 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. In this paper, we built a deep-learning-based automatic workflow named ESPRH for automatically building submarine earthquake catalogs from continuous seismograms. 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. Submarine active faults and earthquakes, which contain crucial information to seafloor tectonics and submarine geohazards, can be effectively characterized by precise submarine earthquake catalogs. 4Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China.3College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China.2Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing, China.1Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China.Xueshan Wu 1,2,3, Song Huang 1,2*, Zhuowei Xiao 2,3,4 and Yuan Wang 1,2
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