Application of The Approximation Neural Network Method for Interpretation of Geoelectric Field Data

Авторы: Obornev I.E., Shimelevich M.I., Obornev E.A., Rodionov E.A.
Сборник: Data Science in Oil and Gas 2021
Тезисы
Год издания: 2021
Издательство: European Association of Geoscientists and Engineers
Местоположение издательства: Netherlands
Первая страница: 1
Последняя страница: 5
DOI: 10.3997/2214-4609.202156020
Аннотация: The paper presents an example of the application of approximation neural network structures to the problem of reconstructing the resistivity distributions of 2D and 3D piecewise linear media from geoelectric data. This problem is reduced to solving a nonlinear operator equation of the first kind. An algorithm was proposed [ Shimelevich et al, 2018 , Obornev et al, 2020 ] for finding an approximate solution of this equation with a total number of parameters of the order of ∼ n 10 ^ 3, based on the use of neural (Kolmogorov) networks of the multilayer perceptron type. This approach, which allows real-time data inversion, is illustrated both on model examples and on profile and areal field survey data.
Добавил в систему: Оборнев Иван Евгеньевич

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