P‐Wave First‐Motion Polarity Determination Using Order Statistics and Entropy Theory (POSE) With Applications to Southeastern Tibetan Plateau

Abstract The Focal Mechanism Solutions (FMSs) of small earthquakes provide valuable insights into crustal structure and stress conditions, while its reliability is limited by the accuracy of first‐motion polarity determination, which remains challenging. In this study, we develop an FMS determination workflow based on a rule‐based Polarity picker using Order Statistics and Entropy theory (POSE). The performance of POSE is compared with two representative deep learning‐based approaches (CNN, Ross et al., 2018, https://doi.org/10.1029/2017jb015251 ; APP, L. Zhang et al., 2023, https://doi.org/10.1785/0220220247 ) in two tectonically diverse regions: Southern California and Southeastern Tibetan Plateau. In Southern California, we evaluate the polarity picking accuracy using manual labels as reference. All methods achieve comparable picking accuracy, while POSE and APP identify twice as many polarities previously labeled as “unknown,” indicating higher picking sensitivity. Moreover, POSE exhibits superior stability against noise levels and picking uncertainties. We then compute FMS from the polarities derived by each method. In Southern California, all algorithms produced similar number of FMSs with a comparable ratio of high‐quality solutions. In more tectonically complex Southeastern Tibet, POSE yields approximately twice as many FMSs as APP and ∼40% more than CNN. Analysis of POSE‐derived FMS catalog reveals fine‐scale variations in faulting style and stress orientation along the Xianshuihe‐Xiaojiang Fault Zone that are not resolved in previous FMS studies but consistent with GPS‐constrained regional strain fields. These results highlight the effectiveness and cross‐regional generalizability of POSE, demonstrating its potential to enhance the resolution and reliability of stress field characterization from small‐magnitude earthquake data sets.