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

Xueshan Wu,Zhuowei Xiao,Song Huang,Yuan Wang

doi:10.3389/feart.2022.817551

Abstract

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.

Highlights

Submarine seismicity and active faults are essential for the analysis and monitoring of submarine geohazards
The combination of Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) detects ~7.5 times more earthquakes in the REAL catalog than using EqT only. This shows the necessity of S-EqT in the first stage
The shallow earthquakes in catalogs, especially at the south side of the Challenger Deep, increased significantly after applying S-EqT, which is important for analyzing the change of seafloor topography and the coupling relationship between seafloor and seawater

Summary

Introduction

Submarine seismicity and active faults are essential for the analysis and monitoring of submarine geohazards. Seismic data is generally extensive, and it is subjective and time-consuming to extract earthquake signals by human experts manually. Building Local Submarine Earthquake Catalogs 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). 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. Its computational cost is relatively high, and sufficient templates are generally unavailable for the OBS network due to the lack of historical observations

Methods

Results

Conclusion