Abstract
The inverse problem (IP) of exploration geophysics consists in reconstructing the spatial distributionf of the properties of the medium in the Earth’s interior from measurements on its surface. This IP is a non-linear ill-posed ill-conditioned problem with high dimensionality both by input and by output. One of the approaches free of many shortcomings inherent for traditional methods of IP solving, is the use of artificial neural networks (NN). In this study, it has been suggested to use an integration of geophysical methods to improve the quality of the solution obtained by NN. The considered model combines three geophysical methods: gravimetry, magnetometry, and magnetotellurics. The problem considered is that of determining the structural boundaries separating the geological layers with constant values of the parameters: density in gravimetry, magnetization in magnetometry, electrical resistivity in magnetotellurics. In this study, a four-layer 2D model was considered. It is demonstrated that integration of geophysical methods provides significantly better results that use of each of the methods separately. It is also shown that in some cases it is also possible to improve the quality of the IP solution using multitask learning—simultaneous determination of the positions of two or all three layer boundaries.
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Acknowledgements
This study has been performed at the expense of the grant of the Russian Science Foundation (project no. 19-11-00333).
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Isaev, I., Obornev, I., Obornev, E., Rodionov, E., Shimelevich, M., Dolenko, S. (2022). Integration of Geophysical Methods for Solving Inverse Problems of Exploration Geophysics Using Artificial Neural Networks. In: Kosterov, A., Bobrov, N., Gordeev, E., Kulakov, E., Lyskova, E., Mironova, I. (eds) Problems of Geocosmos–2020. Springer Proceedings in Earth and Environmental Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-91467-7_7
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