AI-PAL: Self-Supervised AI Phase Picking via Rule-Based Algorithm for Generalized Earthquake Detection
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.
Copyright and License
© 2025 The Author(s).
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Acknowledgement
We thank the JGR editor Rachel Abercrombie, the associate editor, and two anonymous reviewers for their constructive comments on this work. We thank Prof. Ziyadin Cakir for sharing the rupture model and afterslip model for the 2020 Elaziğ (Turkey) earthquake. Additionally, Yijian Zhou would like to thank Dr. Yun Wang from Carnegie Mellon University (now at Facebook) for inspiring the application of RNN sequence labeling in the phase picking task, which should have been acknowledged in Zhou et al. (2019). This work is jointly supported by the National Key R&D Program (Grant 2022YFF0800601) and the University of California, Riverside Senate CoR grant.
Data Availability
Figures in this paper are plotted with GMT and Matplotlib. The continuous seismic data for Ridgecrest-Coso (2008–2019), the waveform-relocated SCSN catalog, the QTM catalog, and the refined focal mechanism solutions by Cheng et al. (2023) are downloaded from the SCSN (SCEDC, 2013). The continuous data for the East Anatolian Fault Zone (EAFZ, 2020–2023/04) is collected through multiple sources: (a) TU network, from the Disaster and Emergency Management Presidency (AFAD, Last accessed 2024/11), downloaded manually, (b) KO network, from Kandilli Observatory And Earthquake Research Institute (KOERI), downloaded with Obspy, and (c) GE, CQ, and IM network, all available through Obspy. The relocated AFAD catalog by Lomax (2023) is available at Zenodo. The focal mechanism solutions for Mw > 4 events (2020–2023) are also available at AFAD. The active faults data of the Ridgecrest-Coso region is available at United States Geological Survey (USGS, Last accessed 2024/11); that for East Anatolian Fault Zone comes from the GEM Global Active Faults Database (Styron & Pagani, 2020), and we adopt a more detailed fault data for the EPF area from the Active Faults of Eurasia Database (AFEAD, Zelenin et al., 2022). The surface rupture data of the 2019 Ridgecrest earthquake comes from Ponti et al. (2020); the fault traces of the 2023 Turkey sequence is available by USGS (2024). The coseismic slip model of the 2019 Ridgecrest earthquake comes from Yue et al. (2021); that for the 2023 Turkey earthquake comes from Ren et al. (2024); the rupture model and afterslip model for the 2020 Elaziğ (Turkey) earthquake comes from Cakir et al. (2023). The referred velocity models for the RC-Coso region include: CVM-S4 velocity model (Kohler et al., 2003), Hutton et al. (2010) for the whole Southern California, and Shelly (2020) for the Ridgecrest source region. The referred velocity models for the EAFZ include that from Güvercin et al. (2022), Acarel et al. (2019), and Ding et al. (2023). The AI-PAL workflow is open sourced at Github (Zhou, 2024).
Supplemental Material
Errata
The originally published version of this article contained typographical errors. Throughout the article, the section headings were misnumbered. The errors have been corrected, and this may be considered the authoritative version of record.
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Additional details
- University of California, Riverside
- National Science and Technology Council
- National Key R&D Program 2022YFF0800601
- Accepted
-
2025-03-14
- Available
-
2025-03-28Version of Record online
- Caltech groups
- Division of Geological and Planetary Sciences (GPS)
- Publication Status
- Published