Iterative Feature Selection with Redundancy Accounting for the Neural Network Solution of Inverse Problems of Magnetotelluric Sounding

Авторы: Shchurov N., Isaev I., Obornev I., Obornev E., Rodionov E., Shimelevich M., Dolenko S.
Сборник: Problems of Geocosmos-2022
Серия: Springer Proceedings in Earth and Environmental Sciences
Год издания: 2023
Место издания: Springer Cham
Первая страница: 111
Последняя страница: 119
DOI: 10.1007/978-3-031-40728-4_8
Аннотация: In this paper, we consider a neural network solution of the inverse problem (IP) of magnetotelluric sounding (MTS) which consists in constructing the electrical conductivity distribution in the Earth’s interior from the values of the electromagnetic field components measured on its surface. It has a high input dimension (thousands of features), so it is necessary to reduce the input data dimension to achieve a more accurate and stable solution while reducing computational complexity. Neighboring measurement points and neighboring frequencies carry similar information dictating the need to use a selection method that considers this feature. The present work is devoted to the study of a method based on the iterative selection of features with the highest correlation with respect to the target variable and the exclusion of features with high cross-correlation. This method was compared with the traditional selection method, the cross-correlation filter.
Добавил в систему: Доленко Сергей Анатольевич

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