Seismo Lab Brown Bag Seminar

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. We developed 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. In this talk, I will first demonstrate the principles of LoSAR, and then apply it to the East Anatolian Fault Zone (EAFZ, 2020-2023/04). Compared to 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 enhanced long-term catalog and b-value analysis, we find that 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.