Neural network algorithm for solving 3D inverse problem of geoelectricsстатья

Работа с статьей


[1] Neural network algorithm for solving 3d inverse problem of geoelectrics / M. I. Shimelevich, E. A. Obornev, I. E. Obornev et al. // Practical and Theoretical Aspects of Geological Interpretation of Gravitational, Magnetic and Electric Fields. — Springer Nature Switzerland AG, 2019. 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. [ DOI ]

Публикация в формате сохранить в файл сохранить в файл сохранить в файл сохранить в файл сохранить в файл сохранить в файл скрыть