Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data. / Gadylshin, Kirill; Silvestrov, Ilya; Bakulin, Andrey.
в: Geophysical Journal International, Том 223, № 3, 01.12.2020, стр. 1888-1898.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data
AU - Gadylshin, Kirill
AU - Silvestrov, Ilya
AU - Bakulin, Andrey
N1 - Publisher Copyright: © 2020 The Author(s) 2020. Published by Oxford University Press on behalf of The Royal Astronomical Society. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - We propose an advanced version of non-linear beamforming assisted by artificial intelligence (NLBF-AI) that includes additional steps of encoding and interpolating of wavefront attributes using inpainting with deep neural network (DNN). Inpainting can efficiently and accurately fill the holes in waveform attributes caused by acquisition geometry gaps and data quality issues. Inpainting with DNN delivers excellent quality of interpolation with the negligible computational effort and performs particularly well for a challenging case of irregular holes where other interpolation methods struggle. Since conventional brute-force attribute estimation is very costly, we can further intentionally create additional holes or masks to restrict expensive conventional estimation to a smaller subvolume and obtain missing attributes with cost-effective inpainting. Using a marine seismic data set with ocean bottom nodes, we show that inpainting can reliably recover wavefront attributes even with masked areas reaching 50-75 per cent. We validate the quality of the results by comparing attributes and enhanced data from NLBF-AI and conventional NLBF using full-density data without decimation.
AB - We propose an advanced version of non-linear beamforming assisted by artificial intelligence (NLBF-AI) that includes additional steps of encoding and interpolating of wavefront attributes using inpainting with deep neural network (DNN). Inpainting can efficiently and accurately fill the holes in waveform attributes caused by acquisition geometry gaps and data quality issues. Inpainting with DNN delivers excellent quality of interpolation with the negligible computational effort and performs particularly well for a challenging case of irregular holes where other interpolation methods struggle. Since conventional brute-force attribute estimation is very costly, we can further intentionally create additional holes or masks to restrict expensive conventional estimation to a smaller subvolume and obtain missing attributes with cost-effective inpainting. Using a marine seismic data set with ocean bottom nodes, we show that inpainting can reliably recover wavefront attributes even with masked areas reaching 50-75 per cent. We validate the quality of the results by comparing attributes and enhanced data from NLBF-AI and conventional NLBF using full-density data without decimation.
KW - Image processing
KW - Neural networks
KW - Numerical approximations and analysis
KW - Seismic noise
UR - http://www.scopus.com/inward/record.url?scp=85099657465&partnerID=8YFLogxK
U2 - 10.1093/gji/ggaa422
DO - 10.1093/gji/ggaa422
M3 - Article
AN - SCOPUS:85099657465
VL - 223
SP - 1888
EP - 1898
JO - Geophysical Journal International
JF - Geophysical Journal International
SN - 0956-540X
IS - 3
ER -
ID: 27718051