Inverse geo-electromagnetic modeling: a systematic review and bibliometric assessment

Inverse electromagnetic (EM) modeling plays a pivotal role in subsurface exploration, enabling the characterization of the Earth’s electrical properties for various applications, including resource exploration, environmental monitoring, and geohazard assessment. Despite significant advancements in the field, the EM inverse problem remains inherently challenging due to its ill-posed and nonlinear nature. A diverse range of methodologies, including deterministic, non-deterministic, and machine learning-based (ML-based) approaches, have been proposed to address these challenges. However, there is a lack of a comprehensive synthesis that integrates both the theoretical evolution of these methods and their bibliometric performance. This paper addresses this gap by combining a systematic review of modern computational methodologies with a bibliometric assessment of the scientific literature on inverse EM modeling. The systematic review critically evaluates key computational approaches, examining their theoretical foundations, practical applications, and limitations, while the bibliometric assessment provides a quantitative assessment of scientific productivity, trends, and contributions from different nations. This integrated perspective offers a unified overview of the field, identifies emerging research directions, and highlights the state-of-the-art in inverse EM modeling. The findings provide valuable insights for researchers, practitioners, and policymakers, guiding future advancements and fostering interdisciplinary collaboration.