Construction of Long-Term Seismic Catalog with Deep Learning: A Workflow for Localized Self-Attention RNN (LoSAR)

Characterizing fault behaviors prior to large earthquakes through long-term seismicity is crucial for seismic hazard assessment, yet constructing high-resolution catalogs over extended periods poses significant challenges. This study introduces LoSAR, a novel deep learning-driven workflow that enhances phase picking by Localizing a Self-Attention Recurrent neural network with local data, addressing the generalization problem common in data-driven approaches. We apply LoSAR to two distinct regions that are both featured by recent large earthquakes: (1) preseismic period of the Ridgecrest-Coso region (2008-2019), and (2) pre-postseismic period of the East Anatolian Fault Zone (EAFZ, 2020-2023/04). Through detailed comparisons, we demonstrate that LoSAR offers slightly higher detection completeness than the QTM matched filter catalog, while boosts an over 100 times faster processing and a superior temporal stability, avoiding low-magnitude gaps during background periods. Against PhaseNet and GaMMA, two established phase picker and associator, LoSAR proves more scalable and generalizable, achieving roughly 2.5 times more event detections in the EAFZ case, along with a ~7 times higher phase association rate. By leveraging the two enhanced catalogs and b-value analysis, we gain insights into the preseismic fault behaviors: (1) The Ridgecrest faults are characterized by sparse and distributed seismicity across a band of ~20 km, revealing multiple orthogonal preexisting faults; coupled with a low b-value that signifies this area as a persistent asperity; (2) The Erkenek-Pütürge segment of EAFZ exhibits complex fault geometry that forms a persistent rupture barrier, which consists of a hidden conjugate fault system that presents as a ~10-km wide fault zone.