The Main Himalayan Thrust (MHT), where the 2015 MW7.8 Gorkha earthquake occurred, features the most seismicity of any structure in Nepal. The structural complexity of the MHT makes it difficult to obtain a definitive interpretation of deep seismogenic structures. The application of new methods and data in this region is necessary to enhance local seismic hazard analyses. In this study, we used a well-designed machine learning-based earthquake location workflow (LOC-FLOW), which incorporates machine learning phase picking, phase association, absolute location, and double-difference relative location, to process seismic data collected by the Hi-CLIMB and NAMASTE seismic networks. We built a high-precision earthquake catalog of both the quiet-period and aftershock seismicity in this region. The seismicity distribution suggests that the quietperiod seismicity (388 events) was controlled by a mid-crustal ramp and the aftershock seismicity (12,669 events) was controlled by several geological structures of the MHT. The higher-level detail of the catalogs derived from this machine learning method reveal clearer structural characteristics, showing how the flat-ramp geometry and a possible duplex structure affect the depth distribution of the seismic events, and how a tear fault changes this distribution along strike. [ABSTRACT FROM AUTHOR]

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