Machine Learning-Based Earthquake Catalog and Tomography Characterize the Middle-Northern Section of the Xiaojiang Fault Zone

Abstract

Abstract The left-lateral strike-slip Xiaojiang fault is an important tectonic boundary between Sichuan–Yunnan diamond block and the Yangtze block, which accommodated several M > 7.0 damaging earthquakes in the past ∼500 yr, as well as intense tectonic deformation. However, the seismogenesis of its middle-northern section are not well understood due to the lack of dense stations. In this study, we analyze one year of continuous seismic records from November 2019 to November 2020, which are recorded at a recently deployed dense seismic array. We build a high-precision earthquake catalog for the region using our recently developed machine learning-based earthquake location workflow (LOC-FLOW), which consists of machine learning phase picking, phase association, velocity model updating, and station correction, absolute location, and double-difference relative location. We then adopt a double-difference tomography method (tomoDD) to refine locations of 16,000 events and build a high-resolution 3D velocity model using both machine learning differential times and cross-correlation differential times. The seismicity distribution not only delineates detailed geometry of the main fault system but also characterizes several branch faults, including two echelon subfaults crossing the north–south-striking main fault. The velocity model shows strong lateral heterogeneities and exhibits a clear relationship to the seismicity distribution: the boundary of high- and low-velocity regions or high-velocity regions above low-velocity bodies accommodate the majority of earthquakes. The variation of the constructed 3D velocity model can be well explained by geological and tectonic settings of the region. In addition, we identify two seismic gaps, which accumulate stress and imply the potential of hosting future moderate-to-large earthquakes. Our study demonstrates, with the aid of LOC-FLOW and tomoDD, machine learning-based phase picks lead to promising performance in constraining high-precision earthquake catalogs and constructing high-resolution velocity models. Machine learning-based tools are becoming the next generation of routine earthquake analysis.