Study of the Integration of Physical Methods in Neural Network Solution of the Inverse Problem of Exploration Geophysics with Variable Physical Properties of the Medium

Abstract

Exploration geophysics requires solving specific inverse problems — reconstructing the spatial distribution of the medium properties in the thickness of the earth from the geophysical fields measured on its surface. We consider inverse problems of gravimetry, magnetometry, magnetotelluric sounding, and their integration, which means simultaneous use of various geophysical fields to reconstruct the desired distribution. Integration requires the determined parameters for all the methods to be the same. This may be achieved by the spatial statement of the problem, in which the task is to determine the boundaries of geophysical objects. In our previous studies, we considered the parameterization scheme where the inverse problem was to determine the lower boundary of several geological layers. Each layer was characterized by variable values of the depth of the lower boundary along the section, and by fixed values of density, magnetization, and resistivity, both for the layer and over the entire dataset. It was demonstrated that the integration of geophysical methods provides significantly better results than the use of each of the methods separately. The present study considers an extended and more realistic model of data—a parameterization scheme with variable properties of the medium, both along each layer and over the dataset.