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Using Domain Knowledge for Feature Selection in Neural Network Solution of the Inverse Problem of Magnetotelluric Soundingстатья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 7 июля 2021 г.
- Авторы: Isaev Igor, Obornev E.A., Obornev Ivan, Rodionov E.A., Shimelevich Mikhail, Shirokiy Vladimir, Dolenko Sergey
- Журнал: Advances in Intelligent Systems and Computing
- Том: 1310
- Год издания: 2021
- Первая страница: 115
- Последняя страница: 126
- DOI: 10.1007/978-3-030-65596-9_15
- Аннотация: In the present study, using the inverse problem (IP) of magnetotelluric sounding (MTS) as an example, we consider the use of neural networks to solve high-dimensional coefficient inverse problems. To reduce the incorrectness, a complex approach is considered related to the use of narrow classes of geological models, with prior selection of the model class by solving the classification problem by MTS data. Within the framework of this approach, the actual direction of work is to reduce the volume of calculations when re-building the system for another set of geological models. This goal can be achieved by selecting the essential features. The present paper is devoted to the study of the applicability of various selection methods to the MTS IP. Also, in this paper we consider taking into account domain knowledge about the studied object in the process of selection of essential features using methods such as wrapper.
- Добавил в систему: Исаев Игорь Викторович