Comprehensive Analysis of Local Earthquakes in the Eastern Himalaya Through Deep Scanning of the Bhutan Seismic Network

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Abstract
This work presents the implementation of an advanced deep-learning (DL)-based earthquake detection workflow on the Bhutan Pilot Experiment dataset, a five station seismic network that was deployed from January 2002 to March 2003 in Bhutan’s Eastern Himalaya region. Previous studies reported 175 local earthquakes in this dataset, not fully understanding the seismicity potential and tectonic settings of the area. To enhance the detection and location accuracy, we reprocess the dataset using a hybrid-pair-input DL model combined with an expert analyst check, enabling a more comprehensive and precise scanning of seismic events. Central to this approach is the well-known EQTransformer (EQT) model, which serves as a core tool, followed by a Siamese EQTransformer (SEQT) to further reduce the false negative rate through a pair-wise model. To deeply scan all potential events, we set extremely low-threshold parameters for both EQT and SEQT models. This strategy led to the SEQT demonstrating an approximately detected 32% increase in the average for P phases and an average of 14% for S phases in comparison to the EQT model. The identified seismic phases were subsequently linked using the Rapid Earthquake Association and Location method via grid search, resulting in a list of 2457 detected events. A meticulous recheck of these events by an expert analyst to assess their validity and the accuracy of detected phases led to the final count of 887 events. These events, which are more than five times the size of the reported earlier (175) events for this dataset, mainly cluster in eastern and central Bhutan, in particular along the Goalpara lineament, a well-known strike-slip fault, and three concentrated clusters of events with harmonic depth and geographical extents. These findings will enhance seismic hazard assessments and advance geophysical research in the region.

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