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Аппроксимационно-нейросетевой подход к решению обратных задач геоэлектрикистатья Исследовательская статья Электронная публикация
- Авторы: Шимелевич М.И., Оборнев Е.А., Оборнев И.Е., Родионов Е.А.
- Сборник: Engineering and Mining Geophysics
- Год издания: 2018
- Место издания: EAGE
- DOI: 10.3997/2214-4609.201800589
- Аннотация: The approximation neural network method for solving conditionally well-posed nonlinear inverse problems of geophysics is presented. The method is based on the neural network approximation of the inverse operator. The inverse problem is solved in the class of grid (block) models of the medium on a regularized parameterization grid. The construction principle of this grid relies on using the calculated values of the continuity modulus of the inverse operator and its modifications determining the degree of ambiguity of the solutions. The method provides approximate solutions of inverse problems with the maximal degree of detail given the specified degree of ambiguity with the total number of the sought parameters equal to several thousand of the medium. The a posteriori estimates of the degree of ambiguity of the approximated solutions are calculated. The work of the method is illustrated by the example of the 3D inversion of synthesized area data and 2D real geoelectric data by the method MTS and MVS. © 2018European Association of Geoscientists and Engineers EAGE. All rights reserved.
- Добавил в систему: Оборнев Иван Евгеньевич