Abstract

Development of 3D models of the Earth’s crust and upper mantle depth structure using data of the geophysical fields in the regions of active and potentially active volcanoes is an important problem from the standpoints of volcanic activity prediction, comparative geodynamics, and general tectonics. The present work is aimed at creating a geoelectric 3D model of the region in the vicinity of the potentially active Elbrus volcano located near densely populated regions of Russia and Georgia. Electromagnetic (EM) measurements over the entire volcano surface should be used to construct a 3D model of the area but they are difficult to perform due to a complex surface topography and/or inaccessibility of individual regions near the volcano summit. This problem can be solved using a new approach combining the analysis of ground-based and satellite data. This approach is based on the method of maximal correlation similarity (MCS), which makes it possible to fill gaps in EM measurements on the Earth’s surface using geological or geophysical data that correlate with rock resistivity. The MCS method was used to construct a 3D model of the Elbrus volcano resistivity based on the magnetotelluric (MT) data obtained on the submeridional profile that crossed the volcano summit [1]. To fill the gaps in the EM data, we used maps of the tectonic fragmentation of the Earth’s crust which were constructed by Nechaev [11] by deciphering satellite photographs of the Earth’s surface. An analysis of the distribution of the tectonic fragmentation coefficient (TFC) and the resistivity, specified at the same section sites of the submeridional profile, made it possible to distinguish the subset of sites where the correlation coefficient between these two parameters is maximal. The artificial neuronet was subsequently trained to correspond to the TFC parameters and resistivity and was used to reconstruct a 3D resistivity distribution over the TFC values in the entire region.

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