Ivan Obornev

—Neural networks (NNs) are successfully used to solve inverse and other problems in geophysics. The aim of this work, which is a continuation of a series of works by a group of authors, is to improve the efficiency of the NN method for solving nonlinear inverse 3D problems of geoelectrics, based on the construction of the author’s convolutional neural network. The network includes a number of additional special transformations (data compression, suppression of the influence of an unknown background environment, etc.) preceding the training of a classical MLP neural network and adapted to the inverse problem that is being solved. This allows us to formally, excluding the human factor, solve inverse problems of geoelectrics of large dimensions without specifying a first approximation based on data measured in areas whose dimensions exceed the dimensions of the network training area. The inversion speed is a few tens of seconds and does not depend on the physical dimensionality (2D or 3D) of the data. The solution to the inverse problem found using a trained neural network can, if necessary, be refined using a random search method. Numerical results of solving 3D geoelectric problems on model and field data are presented, confirming the stated development parameters.

The inverse problems of exploration geophysics consist in reconstructing the spatial distribution of the medium properties in the thickness of the earth from the geophysical fields that are measured on its surface. This study considers the inverse problems of magnetometry, gravimetry, and magnetotelluric sounding, and also their integration, which means simultaneous use of various geophysical fields to reconstruct the desired distribution. Here we compare the results of neural networks (NN) directly trained on the data of several geophysical methods at once with the results of stacking of NN trained on the data of each geophysical method separately.