Deep Scanning of the Bhutan Eastern Himalaya Seismic Dataset for Local Earthquakes

Seismic networks monitor seismic activities across the globe, recording distinctive events within specific geographical and temporal frames. Whether old or new, each seismic record preserves valuable information, with its extraction relying mainly on the sophistication of the method. This study presents the implementation of an advanced earthquake detection workflow on a relatively old dataset, the Bhutan Pilot Experiment. This temporary five-station seismic network in Eastern Himalaya comprised a set of Broadband sensors deployed for 14 months from January 2002 to March 2003. However, outdated methodologies have limited the analysis of the recorded data, resulting in the reporting of only 175 local microearthquakes in this area. In this study, we reprocess the data using the recently introduced deep-scan Integrated Pair-Input deep learning and Migration Location workflow [1] to detect and locate local earthquakes. The IPIML employs the well-known Earthquake Transformer (EqT) model as its core function for initial phase picking, followed by a pair-input Siamese EQTransformer (S-EqT) to further mitigate the false negative rate using a pair-wise model. The S-EqT step demonstrated an approximately 40% increase in average detected phases compared to the standard EqT model. The detected phases are associated using the Rapid Earthquake Association and Location (REAL) method through grid searching, providing a preliminary list of detected events. This list encompasses 2458 detected events, several times larger than the previously reported catalog for this dataset. These events primarily cluster in central and eastern Bhutan, particularly along the Golpara lineament, a recognized strike-slip fault. The subsequent phase of this study involves precisely locating these events through the implementation of the Migration Location (MIL) method.

References
[1] H. Mohammadigheymasi et al., "IPIML: A Deep-Scan Earthquake Detection and Location Workflow Integrating Pair-Input Deep Learning Model and Migration Location Method," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-9, 2023, Art no. 5914109, doi: 10.1109/TGRS.2023.3293914.