Standard

Inpainting of local wavefront attributes using artificial intelligence. / Gadylshin, Kirill; Silvestrov, Ilya; Bakulin, Andrey.

In: SEG Technical Program Expanded Abstracts, 10.08.2019, p. 2212-2216.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Gadylshin, K, Silvestrov, I & Bakulin, A 2019, 'Inpainting of local wavefront attributes using artificial intelligence', SEG Technical Program Expanded Abstracts, pp. 2212-2216. https://doi.org/10.1190/segam2019-3214642.1

APA

Gadylshin, K., Silvestrov, I., & Bakulin, A. (2019). Inpainting of local wavefront attributes using artificial intelligence. SEG Technical Program Expanded Abstracts, 2212-2216. https://doi.org/10.1190/segam2019-3214642.1

Vancouver

Gadylshin K, Silvestrov I, Bakulin A. Inpainting of local wavefront attributes using artificial intelligence. SEG Technical Program Expanded Abstracts. 2019 Aug 10;2212-2216. doi: 10.1190/segam2019-3214642.1

Author

Gadylshin, Kirill ; Silvestrov, Ilya ; Bakulin, Andrey. / Inpainting of local wavefront attributes using artificial intelligence. In: SEG Technical Program Expanded Abstracts. 2019 ; pp. 2212-2216.

BibTeX

@article{f94d379a18a44d05ac5d887e832be8f8,
title = "Inpainting of local wavefront attributes using artificial intelligence",
abstract = "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.",
author = "Kirill Gadylshin and Ilya Silvestrov and Andrey Bakulin",
note = "Funding Information: The authors would like to thank Maxim Dmitriev (Saudi Aramco) for support of the field study. One of the authors (Kirill Gadylshin) was partially supported by RFBR grant no. 18-35-00253 and the Russian Government Grant MK-670.2019.5. Publisher Copyright: {\textcopyright} 2019 SEG; Society of Exploration Geophysicists International Exposition and 89th Annual Meeting, SEG 2019 ; Conference date: 15-09-2019 Through 20-09-2019",
year = "2019",
month = aug,
day = "10",
doi = "10.1190/segam2019-3214642.1",
language = "English",
pages = "2212--2216",
journal = "SEG Technical Program Expanded Abstracts",
issn = "1052-3812",
publisher = "Society of Exploration Geophysicists",

}

RIS

TY - JOUR

T1 - Inpainting of local wavefront attributes using artificial intelligence

AU - Gadylshin, Kirill

AU - Silvestrov, Ilya

AU - Bakulin, Andrey

N1 - Funding Information: The authors would like to thank Maxim Dmitriev (Saudi Aramco) for support of the field study. One of the authors (Kirill Gadylshin) was partially supported by RFBR grant no. 18-35-00253 and the Russian Government Grant MK-670.2019.5. Publisher Copyright: © 2019 SEG

PY - 2019/8/10

Y1 - 2019/8/10

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85121862260&partnerID=8YFLogxK

U2 - 10.1190/segam2019-3214642.1

DO - 10.1190/segam2019-3214642.1

M3 - Conference article

AN - SCOPUS:85121862260

SP - 2212

EP - 2216

JO - SEG Technical Program Expanded Abstracts

JF - SEG Technical Program Expanded Abstracts

SN - 1052-3812

T2 - Society of Exploration Geophysicists International Exposition and 89th Annual Meeting, SEG 2019

Y2 - 15 September 2019 through 20 September 2019

ER -

ID: 35176588