Abstract
The inverse problem of magnetotelluric sounding is a highly non-linear ill-posed inverse problem with high dimension both at the input and at the output. One way to reduce the incorrectness is to narrow the scope of the problem. In our case, this can be implemented in the form of a complex algorithm, which first makes the choice of one of the narrower classes of geological sections and then performs the solution of the regression inverse problem within the selected class. In the present study, we investigate the effectiveness of the implementation of the first phase of this algorithm. The neural network solution of the problem of classification of magnetotelluric sounding data was considered. We estimate the maximum accuracy of classification, perform search for optimal parameters, and test the results for resilience to noise in the data.
Study performed at the expense of Russian Science Foundation, project no. 14-11-00579.
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References
Spichak, V.V. (ed.): Electromagnetic sounding of the earth’s interior. In: Methods in Geochemistry and Geophysics, vol. 40. Elsevier, Amsterdam (2006)
Zhdanov, M.: Inverse Theory and Applications in Geophysics, 2nd edn. Elsevier, Amsterdam (2015)
Zhdanov, M.S.: Geophysical electromagnetic theory and methods. In: Methods in Geochemistry and Geophysics, vol. 43. Elsevier, Amsterdam (2009)
Isaev, I., Dolenko, S.: Comparative analysis of residual minimization and artificial neural networks as methods of solving inverse problems: test on model data. In: Samsonovich, A., Klimov, V., Rybina, G. (eds.) Biologically Inspired Cognitive Architectures (BICA) for Young Scientists. Advances in Intelligent Systems and Computing, vol. 449, pp. 289–295. Springer, Cham (2016)
Raiche, A.: A pattern recognition approach to geophysical inversion using neural nets. Geophys. J. Int. 105(3), 629–648 (1991)
Van der Baan, M., Jutten, C.: Neural networks in geophysical applications. Geophysics 65(4), 1032–1047 (2000)
Sandham, W., Leggett, M. (eds.): Geophysical applications of artificial neural networks and fuzzy logic. In: Modern Approaches in Geophysics, vol. 21. Springer, Heidelberg (2003)
Hajian, A., Styles, P.: Prior applications of neural networks in geophysics. In: Application of Soft Computing and Intelligent Methods in Geophysics, pp. 71–198. Springer, Heidelberg (2018)
Hidalgo-Silva, H., Gomez-Trevino, E., Swiniarski, R.: Neural network approximation of an inverse functional. In: Proceedings of IEEE World Congress on Computational Intelligence, vol. 5, pp. 3387–3392. IEEE (1994)
Spichak, V., Popova, I.: Artificial neural network inversion of magnetotelluric data in terms of three-dimensional earth macroparameters. Geophys. J. Int. 142(1), 15–26 (2000)
Spichak, V., Fukuoka, K., Kobayashi, T., Mogi, T., Popova, I., Shima, H.: ANN reconstruction of geoelectrical parameters of the Minou fault zone by scalar CSAMT data. J. Appl. Geophys. 49(1–2), 75–90 (2002)
Montahaei, M., Oskooi, B.: Magnetotelluric inversion for azimuthally anisotropic resistivities employing artificial neural networks. Acta Geophys. 62(1), 12–43 (2014)
Shimelevich, M.I., Obornev, E.A.: An approximation method for solving the inverse MTS problem with the use of neural networks. Izv. Phys. Solid Earth 45(12), 1055 (2009)
Isaev, I., Obornev, E., Obornev, I., Shimelevich, M., Dolenko, S.: Increase of the resistance to noise in data for neural network solution of the inverse problem of magnetotellurics with group determination of parameters. In: Villa, A., Masulli, P., Pons Rivero, A. (eds.) ICANN 2016, LNCS, vol. 9886, pp. 502–509. Springer, Cham (2016)
Isaev, I.V., Dolenko, S.A.: Adding noise during training as a method to increase resilience of neural network solution of inverse problems: test on the data of magnetotelluric sounding problem. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds.) Neuroinformatics 2017. Studies in Computational Intelligence, vol. 736, pp. 9–16. Springer, Cham (2018)
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Isaev, I., Obornev, E., Obornev, I., Shimelevich, M., Dolenko, S. (2019). Neural Network Recognition of the Type of Parameterization Scheme for Magnetotelluric Data. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research II. NEUROINFORMATICS 2018. Studies in Computational Intelligence, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-030-01328-8_19
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DOI: https://doi.org/10.1007/978-3-030-01328-8_19
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