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Abstract Delineating fault structures through microseismicity is crucial for earthquake hazard assessment, yet constructing high‐resolution catalogs over extended periods remains challenging. This study introduces AI‐PAL, a novel deep learning‐driven workflow that employs a Self‐Attention RNN (SAR) model trained with detections from PAL, an established rule‐based algorithm (Zhou, Yue, et al., 2021, https://doi.org/10.1785/0220210111 ), for generalized earthquake detection. PAL utilizes short‐term‐average over long‐term‐average algorithm for event detection, ensuring consistent performance across different datasets. AI‐PAL leverages these rule‐based picks as training labels, enabling self‐supervised learning of the SAR model across arbitrary regions, thereby enhancing PAL's detection capabilities. We applied SAR‐PAL to two distinct regions that are featured by recent large earthquakes: (a) the preseismic period of the Ridgecrest‐Coso region (2008–2019), and (b) the pre‐to‐postseismic period of the East Anatolian Fault Zone (EAFZ, 2020–2023/04). Our results demonstrate that SAR‐PAL offers slightly higher detection completeness than the quake template matching matched filter catalog, while boosts over 100 times faster processing and a superior temporal stability, avoiding detection gaps during background periods. Compared to PhaseNet and GaMMA, two widely recognized phase picker and associator, SAR‐PAL proved more scalable, achieving ∼2.5 times more event detections in the EAFZ case, along with a ∼7 times higher phase association rate. We further experimented training PhaseNet and SAR with PAL detections and routine catalogs, and found that no other combinations matched the detection performance of SAR‐PAL. The enhanced catalogs built by SAR‐PAL reveals geometrical complexities of the Ridgecrest faults and the Erkenek‐Pütürge segment of EAFZ, offering insights into their contrasting roles during the large earthquake.
Zhou et al. (Fri,) studied this question.