Abstract
When a multi-parameter inverse problem is solved with artificial neural networks, it is usually solved separately for each determined parameter (autonomous determination). In their preceding studies, the authors have demonstrated that joining parameters into groups with simultaneous determination of the values of all parameters within each group may in some cases improve the precision of solution of inverse problems. In this study, the observed effect has been investigated in respect to its resistance to noise in data. The study has been performed at the example of the inverse problem of magnetotellurics, which has a high dimensionality.
This study has been performed at the expense of the grant of Russian Science Foundation (project no. 14-11-00579).
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References
Zhdanov, M.: Inverse Theory and Applications in Geophysics, 2nd edn., 730 p. Elsevier, London (2015)
Yagola, A., Kochikov, I., Kuramshina, G.: Inverse Problems of Vibrational Spectroscopy, 297 p. De Gruyter, Boston (1999)
Mohammad-Djafari, A. (ed.): Inverse Problems in Vision and 3D Tomography, 468 p. Wiley, New York (2010)
Spichak, V.V. (ed.): Electromagnetic Sounding of the Earth’s Interior. Methods in Geochemistry and Geophysics, vol. 40, 388 p. Elsevier, Amsterdam (2006)
Zhdanov, M.S.: Geophysical Electromagnetic Theory and Methods. Methods in Geochemistry and Geophysics, vol. 43, 848 p. Elsevier, Amsterdam (2009)
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)
Li, M., Verma, B., Fan, X., Tickle, K.: RBF neural networks for solving the inverse problem of backscattering spectra. Neural Comput. Appl. 17(4), 391–397 (2008)
Yang, H., Xu, M.: Solving inverse bimodular problems via artificial neural network. Inverse Probl. Sci. Eng. 17(8), 999–1017 (2009)
Dolenko, S., Isaev, I., Obornev, E., Persiantsev, I., Shimelevich, M.: Study of influence of parameter grouping on the error of neural network solution of the inverse problem of electrical prospecting. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013, Part I. CCIS, vol. 383, pp. 81–90. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41013-0_9
Dolenko, S., Isaev, I., Obornev, E., Obornev, I., Persiantsev, I., Shimelevich, M.: Elaboration of a complex algorithm of neural network solution of the inverse problem of electrical prospecting based on data classification. In: Proceedings of the 10th International Conference Problems of Geocosmos, St. Petersburg, Petrodvorets, pp. 11–16 (2014). http://geo.phys.spbu.ru/materials_of_a_conference_2014/C2014/01_Dolenko.pdf
Zhdanov, M.S.: Geophysical Inverse Theory and Regularization Problems. Methods in Geochemistry and Geophysics, vol. 36, 633 p. Elsevier, Amsterdam (2002)
Dolenko, S., Guzhva, A., Obornev, E., Persiantsev, I., Shimelevich, M.: Comparison of adaptive algorithms for significant feature selection in neural network based solution of the inverse problem of electrical prospecting. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 397–405. Springer, Heidelberg (2009)
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Isaev, I., Obornev, E., Obornev, I., Shimelevich, M., Dolenko, S. (2016). 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) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_59
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