Machine learning-based aftershock seismicity of the 2015 Gorkha earthquake controlled by flat-ramp geometry and a tear fault

The Main Himalayan Thrust (MHT), where the 2015 M_W7.8 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 quiet-period 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.