In this article, the use of adaptive convolutional neural network architectures is examined for solving three-dimensional inverse problems in structural geophysics. An object-oriented approach to modeling geophysical data has been developed, which includes the creation of a database for typical hierarchical structural models (grabens, horsts, normal faults, and reverse faults) and the computation of forward problems. The application of convolutional neural networks—incorporating architectures that extract local features, as well as transfer learning methods—has enabled an improvement in the models’ generalization capabilities and facilitated their adaptation to the specifics of different geophysical classes, compared to multi-layer perceptron-based approaches. The numerical results presented demonstrate the fundamental feasibility of applying the proposed approach to the solution of structural inverse problems in geophysics. #CSOC1120.