In recent years, ambient seismic noise has gained considerable attention in seismology due to its potential to advance understanding of Earth’s subsurface dynamics and monitor anthropogenic activities. This study presents a comprehensive review of ambient seismic noise research (ASNR), by exploring its current status, key research hotspots, and emerging trends through a bibliometric and visual analysis of 3,028 articles indexed in the Web of Science database (1984–2023). Using CiteSpace, the study systematically examines key authors, institutions, and countries, as well as thematic keywords and foundational references. Techniques such as cluster analysis, co-citation network analysis, and burst detection are employed to map the evolution of research fields and identify significant collaboration patterns. The analysis reveals a dramatic increase in research output, particularly since 2004, underscoring the expanding role of ASNR in geophysics, geochemistry, and engineering applications. Additionally, the rising interest in detecting human activities through seismic noise, especially in response to events like the COVID-19 pandemic, highlights the broadening scope of ASNR. Notably, the findings emphasize the pivotal importance of ambient noise tomography, a method that has transformed subsurface imaging techniques. This review not only synthesizes the current research landscape but also highlights critical gaps and emerging opportunities, providing a roadmap for future studies. In particular, it emphasizes advancements in seismic risk mitigation, geotechnical investigations, and the monitoring of human activities, offering a timely review and valuable insights that aligns with the interests of researchers in these fields.
China is a country highly vulnerable to natural disasters, resulting in significant losses in terms of human casualties, injuries, property damage, economic losses, infrastructure destruction, and so on each year. We propose a conceptual model based on the Data Envelopment Analysis model to evaluate regional vulnerability in mainland China using the annual data of Chinese official statistics from 2006 to 2021. The proposed model includes five input variables: regional total population, per capita GDP, population density, GDP per square kilometer, and regional total fixed investment in water conservancy, environment, and public facilities management. Additionally, it incorporates two output variables: affected people and direct economic loss. The results indicate that the vulnerability level generally decreases from West China through Central China to East China. Based on the new classification method proposed in this study, the regions are divided into five areas. These findings can serve as a reference for policymakers in enhancing disaster planning and improving the efficiency of natural disaster prevention.
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