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Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data Тезисы доклада

Конференция The Society of Exploration Geophysicists International Exposition and 89th Annual Meeting, San Antonio, Texas, 2019
15-20 сент. 2019 , San Antonio, Texas
Журнал SEG Technical Program Expanded Abstracts
ISSN: 1052-3812
Вых. Данные Год: 2020, Страницы: 2212-2216 Страниц : 5 DOI: 10.1190/segam2019-3214642.1
Авторы Gadylshin Kirill 1,2 , Silvestrov Ilya 3 , Bakulin Andrey 3
Организации
1 Institute of Petroleum Geology and Geophysics, pr. Koptyug 3, 630090, Novosibirsk, Russia
2 Novosibirsk State University, Pirogova 2 St., 630090, Novosibirsk, Russia
3 EXPEC Advanced Research Center, Saudi Aramco, Dhahran, Saudi Arabia

Реферат: We propose a fast method to calculate local wavefront attributes for 3D prestack seismic data. First step is to compute attributes on a coarse regular or irregular grid in time and space using conventional approaches. Second step is very fast and efficient inpainting of the attributes in remaining locations by artificial intelligence utilizing a specially trained deep neural network. The method incorporates multi-parameter attributes using a special colouring scheme and allows estimation of multiple attributes simultaneously during one run. We demonstrate that inpainting of local wavefront attributes for nonlinear beamforming can greatly speed up prestack enhancement of 3D seismic data. Other applications such as velocity analysis or seismic tomography can be implemented using a similar approach.
Библиографическая ссылка: Gadylshin K. , Silvestrov I. , Bakulin A.
Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data
SEG Technical Program Expanded Abstracts. 2020. P.2212-2216. DOI: 10.1190/segam2019-3214642.1 РИНЦ OpenAlex
Идентификаторы БД:
РИНЦ: 43249257
OpenAlex: W2968199932
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БД Цитирований
OpenAlex 4
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