Аннотация:This study addresses the solution of inverse problems in exploration geophysics using machine learning methods that involve reusing a trained model for different cases. At the same time, inverse problems are characterized by ill-posedness, for the reduction of which an approach is considered based on the indirect use of a priori information by taking it into account when forming a training sample through using narrow models of the media that describe some certain class of geological sections. However, this approach requires obtaining a separate solution for each case, with generation of a separate training dataset and training of a separate machine learning model (or set of models), which reduces usability and increases computational costs. Therefore, in order to reduce the computational cost, this study proposes to use transfer learning methods, which involve training base models on large datasets and fine-tuning them on limited datasets. This work is devoted to the study of the applicability of the transfer learning method in relation to inverse problems of gravimetry, magnetometry and magnetotelluric sounding, as well as their integration.
