Induced seismicity associated with shale-gas development has become an increasing concern for seismic hazard assessment in Southwest China. This study develops a machine-learning–based framework to distinguish natural earthquakes from shale-gas-induced seismicity in Chongqing using seismic waveform records archived in the JOPENS system. A suite of physically interpretable spectral and time-domain features was extracted from preprocessed P- and S-wave windows, with emphasis on frequency-dependent characteristics and P/S spectral amplitude ratios. Induced earthquakes were identified using independent time-based labeling and subsequently evaluated through spatial proximity analysis and waveform characterization. The results show that induced events exhibit pronounced high-frequency spectral enrichment and strong spatial clustering around hydraulic fracturing platforms, with most events occurring within approximately 3 km and a median distance of 2.1 km from the nearest platform. An optimized Support Vector Machine classifier achieves an overall accuracy of 0.96 on independent test data, correctly identifying 15 of 16 induced earthquakes and all 12 natural earthquakes, with precision and recall exceeding 0.92 for both classes. These findings demonstrate that combining physically interpretable waveform features with spatial constraints provides a robust and transparent approach for distinguishing induced from natural seismicity, offering practical value for real-time monitoring and seismic hazard mitigation in shale-gas development regions.

The Abuduo Fault is a Holocene left-lateral strike-slip fault located on the eastern margin of the Tibetan Plateau, potentially connecting eastward with the Garzê–Yushu Fault. Due to its high-altitude setting, this fault remains poorly studied, and knowledge of its detailed surface geometry and slip rate is still insufficient. Using GF-7 and other multi-source remote sensing data, field surveys, semi-automatic offset extraction software, and radiocarbon (¹⁴C) dating, we determined the fault’s fine surface geometry, offsets, and slip rate. The results show that the fault can be divided into western, central, and eastern segments based on geometric discontinuities. The central segment consists of four right-stepping en echelon faults. The western and central segments are separated by a left-stepping compressional ridge with a step-over width of ~3.1 km, while the central and eastern segments are separated by a right-stepping pull-apart basin with a step-over width of ~9.4 km. Offsets generally increase from west to east. The western and central segments may exhibit stronger Late Quaternary activity, but this understanding remains to be further validated. Based on offset measurement and the dating of a typical offset gully, the Holocene slip rate of the western segment is estimated at 2.5–2.8 mm/yr.

Earthquake monitoring has changed substantially over the past three decades, yet its technological evolution has rarely been examined through a systematic bibliometric lens. Existing review and scientometric studies have mainly focused on overall publication trends or adjacent disaster domains, with limited attention to how advances in sensing and analysis have reshaped earthquake monitoring itself. They have also paid less attention to how these changes relate to collaboration patterns, thematic structure, and the recent rise of approaches such as artificial intelligence (AI), Distributed Acoustic Sensing (DAS), and GNSS–InSAR integration. To address these issues, this study analyzes earthquake monitoring research published between 1990 and 2024 using records retrieved from the Web of Science Core Collection. VOSviewer and CiteSpace were used to examine publication trends, collaboration networks, co-citation structures, keyword evolution, and influential benchmark studies. Across the analyzed corpus, publication, keyword, and co-citation patterns support three heuristic temporal partitions: low publication activity and strong visibility of station-based physical analysis during 1990–1999; sustained growth accompanied by broadband-network, GNSS, and InSAR themes during 2000–2018; and, during 2019–2024, rapidly increasing bibliometric prominence of AI-enabled detection, DAS, and GNSS–InSAR integration. Country- and institution-level analyses show that the field remains concentrated in research systems supported by sustained infrastructure investment, strong data stewardship, and active international collaboration. Co-citation and keyword patterns further suggest that recent methodological change has developed alongside strong continuity with classical seismology. Overall, the literature shows increasing thematic connectivity among automation, dense sensing, and multi-sensor integration; these signals indicate emerging research attention rather than demonstrated operational superiority.