Аннотация:This study addresses inverse problems in exploration geophysics aimed at reconstructing the spatial distribution of subsurface medium properties from surface measurements of gravitational, magnetic, and electromagnetic fields. The integration of these geological techniques enhances the quality of solutions, but their practical implementation is limited by the availability of site-specific training data. To address this issue, we explore transfer learning approaches, comparing various neural network architectures and training configurations. Our findings indicate that optimized combinations of architecture and training outperform traditional methods, reducing reconstruction errors, maintaining reliability in noisy environments, and achieving comparable accuracy with smaller target training datasets.
