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

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Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data. / Gadylshin, Kirill; Silvestrov, Ilya; Bakulin, Andrey.

In: Geophysical Journal International, Vol. 223, No. 3, 01.12.2020, p. 1888-1898.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{29838c9e50774ae28f1c791351c17aa6,

title = "Inpainting of local wavefront attributes using artificial intelligence for enhancement of massive 3-D pre-stack seismic data",

abstract = "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. ",

keywords = "Image processing, Neural networks, Numerical approximations and analysis, Seismic noise",

author = "Kirill Gadylshin and Ilya Silvestrov and Andrey Bakulin",

note = "Publisher Copyright: {\textcopyright} 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.",

year = "2020",

month = dec,

day = "1",

doi = "10.1093/gji/ggaa422",

language = "English",

volume = "223",

pages = "1888--1898",

journal = "Geophysical Journal International",

issn = "0956-540X",

publisher = "Oxford University Press",

number = "3",

}

RIS

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

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