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Multitasking learning in missing data recovery for the integration of geophysical methods in solving an inverse problem of exploration geophysicsстатья Исследовательская статья
- Авторы: Isaev I.V., Obornev I.E., Obornev E.A., Rodionov E.A., Shimelevich M.I., Dolenko S.A.
- Журнал: Procedia computer science
- Том: 213
- Год издания: 2022
- Издательство: Elsevier BV
- Местоположение издательства: Netherlands
- Первая страница: 777
- Последняя страница: 784
- DOI: 10.1016/j.procs.2022.11.134
- Аннотация: In their previous studies, the authors have shown that the integration of geophysical methods allows improving the quality of the solution of an inverse problem of exploration geophysics in comparison with the individual use of each of them. However, in practice, it is possible that for some measurement points, data from one of the geophysical methods used is missing. In this study, we investigate an approach associated with neural network recovery of the missing data of one geophysical method from the known data of another, and their further joint application to solve the inverse problem. In addition, we explore the effectiveness of applying multitask learning approach at the data recovery stage for the subsequent solution of the inverse problem.
- Добавил в систему: Доленко Сергей Анатольевич