Standard

Automatic detection of channels in seismic images via deep learning neural networks. / Krasnov, Fedor V.; Butorin, Alexander V.; Sitnikov, Alexander N.

In: Business Informatics, No. 2, 2018, p. 7-16.

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

Harvard

Krasnov, FV, Butorin, AV & Sitnikov, AN 2018, 'Automatic detection of channels in seismic images via deep learning neural networks', Business Informatics, no. 2, pp. 7-16. https://doi.org/10.17323/1998-0663.2018.2.7.16

APA

Krasnov, F. V., Butorin, A. V., & Sitnikov, A. N. (2018). Automatic detection of channels in seismic images via deep learning neural networks. Business Informatics, (2), 7-16. https://doi.org/10.17323/1998-0663.2018.2.7.16

Vancouver

Krasnov FV, Butorin AV, Sitnikov AN. Automatic detection of channels in seismic images via deep learning neural networks. Business Informatics. 2018;(2):7-16. https://doi.org/10.17323/1998-0663.2018.2.7.16

Author

Krasnov, Fedor V. ; Butorin, Alexander V. ; Sitnikov, Alexander N. / Automatic detection of channels in seismic images via deep learning neural networks. In: Business Informatics. 2018 ; No. 2. pp. 7-16.

BibTeX

@article{25614e40d51e4f2d912d9947eadd4ac5,
title = "Automatic detection of channels in seismic images via deep learning neural networks",
abstract = "The business goal of interpreting seismic data has always been addressed by the high-level experts engaged. The authors applied a computer vision approach to interpret seismic data. The expert task of interpreting seismic data has become partially automated via machine learning techniques utilized to classify the images used by the authors. The methods of transformation of seismic traces through spectral decomposition were used to obtain the data set. In the previous works of the authors, methods of spectral decomposition via continuous wavelet transformation were created, and this also laid the foundation of this study. Use of artificial neural networks of deep learning has enabled the authors to meet the goal of image classification. In this regard, it is important to note that the business policy related to information dissemination imposed certain limitations on the computing capacity used and the number of the data labeled. The solution found for the use of trained artificial neural networks and image augmentation helped us to successfully cope with the goal, in spite of the limitations. The results obtained allow us to identify geological units with a test accuracy of 90% rendering to the F1-score measure. This has enabled the Scientific and Technical Center of Gazprom Neft to implement automated procedures in the existing business processes in order to significantly reduce the time needed to process seismic data. The authors consider the possibility of “digitizing” and preserving the knowledge of the highest-level experts in interpreting seismic data, as well as the possibility of using contactless examination to locate geological units in the seismic data within the Gazprom Neft group of companies to be a socially efficient outcome of this study.",
keywords = "Computer vision, Geological units, Seismic, Small datasets, Spectral decomposition, Supervised machine learning",
author = "Krasnov, {Fedor V.} and Butorin, {Alexander V.} and Sitnikov, {Alexander N.}",
note = "Publisher Copyright: {\textcopyright} 2020 National Research University, Higher School of Econoimics.",
year = "2018",
doi = "10.17323/1998-0663.2018.2.7.16",
language = "English",
pages = "7--16",
journal = "Business Informatics",
issn = "2587-814X",
publisher = "National Research University, Higher School of Econoimics",
number = "2",

}

RIS

TY - JOUR

T1 - Automatic detection of channels in seismic images via deep learning neural networks

AU - Krasnov, Fedor V.

AU - Butorin, Alexander V.

AU - Sitnikov, Alexander N.

N1 - Publisher Copyright: © 2020 National Research University, Higher School of Econoimics.

PY - 2018

Y1 - 2018

N2 - The business goal of interpreting seismic data has always been addressed by the high-level experts engaged. The authors applied a computer vision approach to interpret seismic data. The expert task of interpreting seismic data has become partially automated via machine learning techniques utilized to classify the images used by the authors. The methods of transformation of seismic traces through spectral decomposition were used to obtain the data set. In the previous works of the authors, methods of spectral decomposition via continuous wavelet transformation were created, and this also laid the foundation of this study. Use of artificial neural networks of deep learning has enabled the authors to meet the goal of image classification. In this regard, it is important to note that the business policy related to information dissemination imposed certain limitations on the computing capacity used and the number of the data labeled. The solution found for the use of trained artificial neural networks and image augmentation helped us to successfully cope with the goal, in spite of the limitations. The results obtained allow us to identify geological units with a test accuracy of 90% rendering to the F1-score measure. This has enabled the Scientific and Technical Center of Gazprom Neft to implement automated procedures in the existing business processes in order to significantly reduce the time needed to process seismic data. The authors consider the possibility of “digitizing” and preserving the knowledge of the highest-level experts in interpreting seismic data, as well as the possibility of using contactless examination to locate geological units in the seismic data within the Gazprom Neft group of companies to be a socially efficient outcome of this study.

AB - The business goal of interpreting seismic data has always been addressed by the high-level experts engaged. The authors applied a computer vision approach to interpret seismic data. The expert task of interpreting seismic data has become partially automated via machine learning techniques utilized to classify the images used by the authors. The methods of transformation of seismic traces through spectral decomposition were used to obtain the data set. In the previous works of the authors, methods of spectral decomposition via continuous wavelet transformation were created, and this also laid the foundation of this study. Use of artificial neural networks of deep learning has enabled the authors to meet the goal of image classification. In this regard, it is important to note that the business policy related to information dissemination imposed certain limitations on the computing capacity used and the number of the data labeled. The solution found for the use of trained artificial neural networks and image augmentation helped us to successfully cope with the goal, in spite of the limitations. The results obtained allow us to identify geological units with a test accuracy of 90% rendering to the F1-score measure. This has enabled the Scientific and Technical Center of Gazprom Neft to implement automated procedures in the existing business processes in order to significantly reduce the time needed to process seismic data. The authors consider the possibility of “digitizing” and preserving the knowledge of the highest-level experts in interpreting seismic data, as well as the possibility of using contactless examination to locate geological units in the seismic data within the Gazprom Neft group of companies to be a socially efficient outcome of this study.

KW - Computer vision

KW - Geological units

KW - Seismic

KW - Small datasets

KW - Spectral decomposition

KW - Supervised machine learning

UR - http://www.scopus.com/inward/record.url?scp=85066621093&partnerID=8YFLogxK

U2 - 10.17323/1998-0663.2018.2.7.16

DO - 10.17323/1998-0663.2018.2.7.16

M3 - Article

AN - SCOPUS:85066621093

SP - 7

EP - 16

JO - Business Informatics

JF - Business Informatics

SN - 2587-814X

IS - 2

ER -

ID: 88695644