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The Use of a priori Information in the Neural Network Solution of the Inverse Problem of Exploration Geophysicsстатья Исследовательская статья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 15 февраля 2024 г.
- Авторы: Isaev Igor, Obornev Ivan, Obornev Eugeny, Rodionov Eugeny, Shimelevich Mikhail, Dolenko Sergey
- Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research VII
- Серия: Studies in Computational Intelligence
- Том: 1120
- Год издания: 2023
- Издательство: Springer Nature
- Местоположение издательства: Switzerland
- Первая страница: 455
- Последняя страница: 464
- DOI: 10.1007/978-3-031-44865-2_48
- Аннотация: This study is devoted to solving inverse problems of exploration geophysics, which consist in reconstructing the spatial distribution of the properties of the medium in the thickness of the earth from the geophysical fields measured on its surface. We consider the methods of gravimetry, magnetometry, and magnetotelluric sounding, as well as their integration, i.e. simultaneous use of data from several geophysical methods to solve the inverse problem. To implement such integration, in our previous studies we have proposed a parameterization scheme that describes a layered geophysical model with fixed layer properties, in which the determined parameters were the positions of the boundaries between the layers. In the present study, this parameterization scheme is complicated so that the properties of the layers vary from pattern to pattern in the data set. To improve the quality of neural network solution of the described inverse problem, we consider an approach based on the use of a priori information about the physical properties of the layers, in which this information is used directly as additional input features for the neural network.
- Добавил в систему: Доленко Сергей Анатольевич