3D Neural Network Inversion of Field Geoelectric Data with Calculating Posterior Estimates

Abstract—The approximation neural network (ANN) method for solving the inverse geoelectric problem in the piecewise constant classes of media with the subsequent refinement of the model by random search method is presented. A conditionally well-posed magnetotelluric inverse problem is considered on a regularized parameterization grid. The total number of the sought-for parameters in this problem can be n 1{0^{3}}. The work of the algorithm is illustrated by the example of 3D inversion of the field survey data. The posterior ambiguity characteristics of the obtained solution were calculated. The proposed methods are implemented using supercomputing cluster resources and parallel computing technology.