Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System

M. K. Khan; Y. Bashir; S. Dossary; S. S. Ali; G. Janampa Ananos

doi:10.3997/2214-4609.2022616010

Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System

M. K. Khan, Y. Bashir, S. Dossary, S. S. Ali, G. Janampa Ananos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This paper present a hybrid Machine Learning and seismic structural attributes approach to extract detailed major and minor discontinuities from seismic data from a complex fault system that present a challenge using conventional interpretation techniques. Fault interpretation is image segmentation problem and we thus adopted U-net encoder-decoder architecture as a first step in this hybrid workflow, it is well suited for seismic discontinuities. Image segmentation can not only figure out whether a particular feature such as faults exist but can also create a mask showing where in the volume those features exist.

Original language	English
Title of host publication	2nd EAGE Subsurface Intelligence Workshop
Publisher	European Association of Geoscientists and Engineers, EAGE
ISBN (Electronic)	9789462824409
DOIs	https://doi.org/10.3997/2214-4609.2022616010
Publication status	Published - 2022
Externally published	Yes
Event	2nd EAGE Subsurface Intelligence Workshop 2022 - Manama, Bahrain Duration: 28 Oct 2022 → 31 Oct 2022

Publication series

Name	2nd EAGE Subsurface Intelligence Workshop

Conference

Conference	2nd EAGE Subsurface Intelligence Workshop 2022
Country/Territory	Bahrain
City	Manama
Period	28/10/22 → 31/10/22

Bibliographical note

Publisher Copyright:
© 2nd EAGE Subsurface Intelligence Workshop 2022.

Access to Document

10.3997/2214-4609.2022616010

Cite this

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title = "Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System",

abstract = "This paper present a hybrid Machine Learning and seismic structural attributes approach to extract detailed major and minor discontinuities from seismic data from a complex fault system that present a challenge using conventional interpretation techniques. Fault interpretation is image segmentation problem and we thus adopted U-net encoder-decoder architecture as a first step in this hybrid workflow, it is well suited for seismic discontinuities. Image segmentation can not only figure out whether a particular feature such as faults exist but can also create a mask showing where in the volume those features exist.",

author = "Khan, {M. K.} and Y. Bashir and S. Dossary and Ali, {S. S.} and Ananos, {G. Janampa}",

note = "Publisher Copyright: {\textcopyright} 2nd EAGE Subsurface Intelligence Workshop 2022.; 2nd EAGE Subsurface Intelligence Workshop 2022 ; Conference date: 28-10-2022 Through 31-10-2022",

year = "2022",

doi = "10.3997/2214-4609.2022616010",

language = "English",

series = "2nd EAGE Subsurface Intelligence Workshop",

publisher = "European Association of Geoscientists and Engineers, EAGE",

booktitle = "2nd EAGE Subsurface Intelligence Workshop",

}

Khan, MK, Bashir, Y, Dossary, S, Ali, SS & Ananos, GJ 2022, Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System. in 2nd EAGE Subsurface Intelligence Workshop. 2nd EAGE Subsurface Intelligence Workshop, European Association of Geoscientists and Engineers, EAGE, 2nd EAGE Subsurface Intelligence Workshop 2022, Manama, Bahrain, 28/10/22. https://doi.org/10.3997/2214-4609.2022616010

Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System. / Khan, M. K.; Bashir, Y.; Dossary, S. et al.
2nd EAGE Subsurface Intelligence Workshop. European Association of Geoscientists and Engineers, EAGE, 2022. (2nd EAGE Subsurface Intelligence Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Khan, M. K.

AU - Bashir, Y.

AU - Dossary, S.

AU - Ali, S. S.

AU - Ananos, G. Janampa

PY - 2022

Y1 - 2022

N2 - This paper present a hybrid Machine Learning and seismic structural attributes approach to extract detailed major and minor discontinuities from seismic data from a complex fault system that present a challenge using conventional interpretation techniques. Fault interpretation is image segmentation problem and we thus adopted U-net encoder-decoder architecture as a first step in this hybrid workflow, it is well suited for seismic discontinuities. Image segmentation can not only figure out whether a particular feature such as faults exist but can also create a mask showing where in the volume those features exist.

AB - This paper present a hybrid Machine Learning and seismic structural attributes approach to extract detailed major and minor discontinuities from seismic data from a complex fault system that present a challenge using conventional interpretation techniques. Fault interpretation is image segmentation problem and we thus adopted U-net encoder-decoder architecture as a first step in this hybrid workflow, it is well suited for seismic discontinuities. Image segmentation can not only figure out whether a particular feature such as faults exist but can also create a mask showing where in the volume those features exist.

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M3 - Conference contribution

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T3 - 2nd EAGE Subsurface Intelligence Workshop

BT - 2nd EAGE Subsurface Intelligence Workshop

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Y2 - 28 October 2022 through 31 October 2022

ER -

Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System

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