Technological evolution and emerging research hotspots in earthquake monitoring from 1990 to 2024 revealed by bibliometric evidence

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.