On 6 February 2023, two large earthquakes (moment magnitude 7.8 and 7.6) shocked a vast area of southeastern Türkiye and northern Syria, leading to heavy casualties and economic loss. To investigate the rupture process over multiple fault segments, we performed a comprehensive analysis of local seismic and geodetic data and determined supershear ruptures on the initial branch and the Pazarcık and Erkenek segments and subshear ruptures on the Amanos segment of event 1. The bilateral rupture of event 2 also presents distinct sub- and supershear velocities. The dynamic stress of the branch fault rupture triggered the Pazarcık segment initial rupture at a point 9 kilometers west of the junction of these two faults, boosting the supershear rupture of the Pazarcık segment of the main fault. The geometry and prestress level of multiple segments controlled the rupture behaviors and influenced the ground shaking intensity.

We developed an earthquake detection and location architecture for continuous seismograms that incorporates phase picking, phase association, location, and matched‐filter techniques (PALM). The PALM architecture incorporates two modules: (1) PAL, the initial detection following picking, association, and location processes, and (2) match, expand, shift, and stack (MESS), a matched‐filter detector that augments the template catalog. The effectiveness of PALM is demonstrated in building an early aftershock catalog for the 2019 Ridgecrest, California, earthquake. By comparing with Southern California Seismic Network arrival times, we show that the PAL picker combines the strengths of short‐term average/long‐term average and the kurtosis picker, realizing robust phase detection and precise picking. Our final MESS catalog is compared with two other matched‐filter catalogs by Ross, Idini, et al. (2019) and Shelly (2020). We find that PALM directly recovers unbiased and detailed features in seismicity from continuous seismograms, which can be efficiently implemented to scan continuous waveforms without the need for visual inspection.

On 5 September 2022, an $Mw$ 6.6 strike‐slip earthquake occurred in Luding, Sichuan, China. It illuminates the seismic gap on the southern end of Xianshuihe fault zone, that is Moxi segment, and is the most damaging earthquake in Chinese Mainland since 2014. In this article, we use multiple seismological methods to analyze the basic characteristics of the Luding earthquake, including its source parameters, rupture process, aftershock distribution, and further discuss its implications on preseismic fault behavior and impact on short‐term seismic hazard. Using near‐field stations, we first revisit the source parameters of the mainshock and obtain a much shallower hypocentral depth (∼9.3 km) than previously reported. Next, we jointly inverse the teleseismic P‐wave data and near‐field strong‐motion data to constrain the mainshock rupture process. The coseismic slip consists of two subevents that spans for ∼20 km and shows an overall unilateral rupture to the south‐southeast direction. The earthquake has 90% of the seismic moment released in its first 10 s. Third, we relocate the first 34 days of aftershocks with both manual phase picks and cross correlation, and finally obtain 5269 well‐located events. The aftershocks reveal a near‐vertical Xianshuihe fault and two sets of unmapped faults off the major Xianshuihe fault: one set of strike‐slip faults on the north‐northwest of rupture and the other one on the west of mainshock that generates the most intense seismicity with prevalent normal faulting. By examining the aftershock evolution and Coulomb stress change, we find that postseismic processes evolve both on and off the major fault, and the western off‐fault system has highly variable fault orientations. Finally, we calculate the Coulomb stress change by the Luding earthquake on surrounding faults. Results show that the north Anninghe fault is significantly loaded by the Luding earthquake (>0.1 MPa), which poses high seismic hazard in the short term.

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.