Model-Driven and Data-Driven Inversion in Geophysical Exploration

Geophysical inversion is the process of inferring subsurface medium properties and structures from surface-observed data, widely applied in fields such as resource and energy exploration, geological surveys, and environmental monitoring. Traditional geophysical inversion relies on mathematical-physical modeling and efficient numerical computations. However, due to the ill-posed nature of geophysical inverse problems and the complexity of subsurface media, purely model-driven computational approaches often suffer from non-uniqueness (multi-solution issues) and numerical instability. To address these challenges, data-driven methods leverage large volumes of observational data, employing statistical analysis and machine learning techniques to uncover hidden patterns within the data, thereby improving the stability and accuracy of inversion results. Artificial intelligence (AI) technologies are now extensively applied in geophysical image processing, reconstruction, and inversion, delivering more precise results for exploration and interpretation. This paper will discuss model-driven geophysical inversion, data-driven geophysical inversion, joint inversion, and AI-enhanced geophysics, supported by case study analyses, especially gravity-magnetic and electromagnetic inversion. We aim to achieve precise subsurface characterization by combining physical models, observational data, and domain knowledge through intelligent computing technologies.