Bibliometric Analysis of Literature on Inverse Problems in Geophysics Using CiteSpace
Year: 2026
Type: article
Abstract: Purpose Research on inverse problems of geophysics is extensive. Thus, there is a need to conduct in-depth and comprehensive bibliometric research to capture the overall development of the field. Design/methodology/approach We conduct a bibliometric analysis using CiteSpace to reveal dynamic trends in collaboration, co-citation, and co-occurrence in this field. Findings We find that publications in the field have shown a fluctuating upward trend since 2004, accompanied by a gradual increase in its proportion within the broader geophysics’ literature. In terms of institutional collaborations, Chinese and French research institutions occupy an important position in the global collaboration network. From a co-citation perspective, Geophysics is the most cited journal, and Araya-Polo Mauricio’s article (Araya-Polo, M., J. Jennings, A. Adler, and T. Dahlke. 2018. “Deep-Learning Tomography.” The Leading Edge 37 (1): 58–66) is the most cited reference. From the perspective of keyword co-occurrence, the keyword clusters show the diversity of geophysical inversion research. The research topics cover fundamental theory, method development, and practical applications. In addition, future research may focus more on research based on data-driven workflows and artificial intelligence. Research limitations Only the SCI and SSCI editions in the Web of Science database were used as the data source, ignoring the literature in other databases. We focused on the literature from 2004 to 2023, which excludes the latest literature in 2024 and earlier literature before 2004. Practical implications We provided a comprehensive and specific analysis of the literature in the field over the last 20 years to help readers gain a comprehensive understanding of the current state of research, hotspots, evolution, and trends in the field. We discussed current research challenges and possible future research directions to help researchers identify appropriate research directions and conduct subsequent research more efficiently. Originality/value Compared with previous related studies, this study is innovative as we provide a comprehensive and specific analysis of the literature in the field over the last 20 years to help readers gain a comprehensive understanding of the current state of research, hotspots, evolution, and trends in the field and discuss current research challenges and possible future research directions to help researchers identify appropriate research directions and conduct subsequent research more efficiently.
Authors: Jiajia Liu, Hanyue Sun, Robin Haunschild
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7169592196