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

2020. 2212-2216 Paper presented at Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019, San Antonio, United States.

Research output: Contribution to conference › Paper › peer-review

Harvard

Gadylshin, K, Silvestrov, I & Bakulin, A 2020, 'Inpainting of local wavefront attributes using artificial intelligence', Paper presented at Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019, San Antonio, United States, 15.09.2019 - 20.09.2019 pp. 2212-2216. https://doi.org/10.1190/segam2019-3214642.1

APA

Gadylshin, K., Silvestrov, I., & Bakulin, A. (2020). Inpainting of local wavefront attributes using artificial intelligence. 2212-2216. Paper presented at Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019, San Antonio, United States. https://doi.org/10.1190/segam2019-3214642.1

Vancouver

Gadylshin K, Silvestrov I, Bakulin A. Inpainting of local wavefront attributes using artificial intelligence. 2020. Paper presented at Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019, San Antonio, United States. doi: 10.1190/segam2019-3214642.1

Author

Gadylshin, Kirill ; Silvestrov, Ilya ; Bakulin, Andrey. / Inpainting of local wavefront attributes using artificial intelligence. Paper presented at Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019, San Antonio, United States.5 p.

BibTeX

@conference{7c78266b3d794c10acee36b770124883,
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 Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019 ; Conference date: 15-09-2019 Through 20-09-2019",
year = "2020",
doi = "10.1190/segam2019-3214642.1",
language = "English",
pages = "2212--2216",

}

RIS

TY - CONF

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 Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

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=85079507578&partnerID=8YFLogxK

U2 - 10.1190/segam2019-3214642.1

DO - 10.1190/segam2019-3214642.1

M3 - Paper

AN - SCOPUS:85079507578

SP - 2212

EP - 2216

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

Y2 - 15 September 2019 through 20 September 2019

ER -

ID: 27734405