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Machine Learning Methods for Interpretation of Multidimensional Geophysical Dataстатья
- Авторы: Shimelevich M.I., Obornev E.A., Obornev I.E., Rodionov E.A.
- Сборник: Geomodel 2019
- Год издания: 2019
- DOI: 10.3997/2214-4609.201950099
- Аннотация: The task of building a geoelectric profile based on geophysical fields measured on the surface can be compared with the task of recognizing an object based on its quantitative characteristics. For example, a face can be recognized by the numerical values characterizing the geometry of its constituent parts. In our case, the role of these quantitative characteristics is played by the measured values of the electromagnetic field, and the restored image is the geoelectric section in the color legend, where the color corresponds to a certain value of the specific resistance of the medium. Approximate neural network methods of geophysics are based on the construction of an approximate inverse problem operator in a given class of media using a multilayered neural network - a neural network approximator (NNA). For the construction of the NNA, the problem of its training is solved, which lies in the fact that the coefficients of the NNA are adjusted by the training sample of known solutions of direct problems obtained using the direct operator of the problem being solved. The task of adjusting the coefficients of the approximator is reduced to an optimization problem, which is solved using the methods of the Monte Carlo group.
- Добавил в систему: Оборнев Иван Евгеньевич