Concurrent Detection of Salt Domes and Faults using ResNet with U-Net

Mustafa Alfarhan; Ahmed Maalej; Mohamed Deriche

doi:10.1109/CDMA47397.2020.00026

Concurrent Detection of Salt Domes and Faults using ResNet with U-Net

Mustafa Alfarhan, Ahmed Maalej, Mohamed Deriche

Department of Electrical Engineering

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

11 Scopus citations

Abstract

Salt domes and faults are important seismic events and constitute potential indicators of hydrocarbon (i.e. oil, gas) accumulation. Seismic data interpretation is one fundamental process for identifying such events and even more. In the last decades a number of techniques have been proposed for automation of the seismic interpretation. The main goals is to speed up the interpretation process and improve interpretation accuracy. In this paper we present a brief overview of important approaches developed for salt and fault identification while categorizing them into feature engineering based and deep learning (DL) based. We present our DL framework for simultaneous salt domes and faults delineation with highlighting the promising preliminary results obtained trough applications to real world seismic datasets.

Original language	English
Title of host publication	Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	118-122
Number of pages	5
ISBN (Electronic)	9781728127460
DOIs	https://doi.org/10.1109/CDMA47397.2020.00026
State	Published - Mar 2020

Publication series

Name	Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Seismic interpretation
convolutional and deconvolutional neural networks
deep learning
faults detection
feature engineering
salt domes delineation

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Information Systems
Information Systems and Management

Access to Document

10.1109/CDMA47397.2020.00026

Cite this

Alfarhan, M., Maalej, A., & Deriche, M. (2020). Concurrent Detection of Salt Domes and Faults using ResNet with U-Net. In Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020 (pp. 118-122). Article 9044229 (Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDMA47397.2020.00026

Alfarhan, Mustafa ; Maalej, Ahmed ; Deriche, Mohamed. / Concurrent Detection of Salt Domes and Faults using ResNet with U-Net. Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 118-122 (Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020).

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abstract = "Salt domes and faults are important seismic events and constitute potential indicators of hydrocarbon (i.e. oil, gas) accumulation. Seismic data interpretation is one fundamental process for identifying such events and even more. In the last decades a number of techniques have been proposed for automation of the seismic interpretation. The main goals is to speed up the interpretation process and improve interpretation accuracy. In this paper we present a brief overview of important approaches developed for salt and fault identification while categorizing them into feature engineering based and deep learning (DL) based. We present our DL framework for simultaneous salt domes and faults delineation with highlighting the promising preliminary results obtained trough applications to real world seismic datasets.",

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Alfarhan, M, Maalej, A & Deriche, M 2020, Concurrent Detection of Salt Domes and Faults using ResNet with U-Net. in Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020., 9044229, Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 118-122. https://doi.org/10.1109/CDMA47397.2020.00026

Concurrent Detection of Salt Domes and Faults using ResNet with U-Net. / Alfarhan, Mustafa; Maalej, Ahmed; Deriche, Mohamed.
Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 118-122 9044229 (Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020).

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

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AB - Salt domes and faults are important seismic events and constitute potential indicators of hydrocarbon (i.e. oil, gas) accumulation. Seismic data interpretation is one fundamental process for identifying such events and even more. In the last decades a number of techniques have been proposed for automation of the seismic interpretation. The main goals is to speed up the interpretation process and improve interpretation accuracy. In this paper we present a brief overview of important approaches developed for salt and fault identification while categorizing them into feature engineering based and deep learning (DL) based. We present our DL framework for simultaneous salt domes and faults delineation with highlighting the promising preliminary results obtained trough applications to real world seismic datasets.

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Alfarhan M, Maalej A, Deriche M. Concurrent Detection of Salt Domes and Faults using ResNet with U-Net. In Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 118-122. 9044229. (Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020). doi: 10.1109/CDMA47397.2020.00026

Concurrent Detection of Salt Domes and Faults using ResNet with U-Net

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this