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Neural network algorithm for solving 3D inverse problem of geoelectricsстатья
- Авторы: Shimelevich M.I., Obornev E.A., Obornev I.E., Rodionov E.A., Dolenko S.A.
- Сборник: Practical and Theoretical Aspects of Geological Interpretation of Gravitational, Magnetic and Electric Fields
- Серия: Springer Proceedings in Earth and Environmental Sciences book series (SPEES)
- Год издания: 2019
- Место издания: Springer Nature Switzerland AG
- DOI: 10.1007/978-3-319-97670-9_9
- Аннотация: Abstract The approximating neural network algorithm for solving the inverse problems of geoelectrics in the class of grid (block) models of the medium is presented. The algorithm is based on constructing an approximate inverse operator using neural networks and makes it possible to formally obtain the solutions of the geoelectrics inverse problem with a total number of the sought parameters of the medium ~n*10^3. The questions concerning the correctness of the problem of constructing the inverse neural network operators are considered. The a posteriori estimates of the degree of ambiguity in the inverse problem solutions are calculated. The work of the algorithm is illustrated by the examples of 2D and 3D inversions of the synthesized data and the real magnetotelluric sounding data.
- Добавил в систему: Оборнев Иван Евгеньевич