Fault Identification of Seismic Data Based on SEU-Net Approach

Ren, Wenbo; Chen, Xuan; Zhu, Xiansheng; Bao, Dian; He, Xinming; Zhao, Yan; Zhao, Ming

doi:10.3390/app151810152

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Open AccessArticle

Fault Identification of Seismic Data Based on SEU-Net Approach

by

Wenbo Ren

^1,2,

Xuan Chen

^1,*

,

Xiansheng Zhu

²,

Dian Bao

²,

Xinming He

²,

Yan Zhao

^1,* and

Ming Zhao

¹

Key Laboratory Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China

²

Exploration and Development Research Institute of Sinopec Northwest Oilfield Company, Urumchi 830011, China

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2025, 15(18), 10152; https://doi.org/10.3390/app151810152

Submission received: 8 August 2025 / Revised: 11 September 2025 / Accepted: 15 September 2025 / Published: 17 September 2025

(This article belongs to the Section Earth Sciences)

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Abstract

Fault interpretation is a key link in seismic data interpretation. With the continuous increase in seismic data volume in original production, manual fault picking cannot efficiently handle massive seismic data. With the development of artificial intelligence technology, the automatic and rapid picking of faults has become a hot topic in the application of deep learning methods in the field of seismic data interpretation. Therefore, this paper proposes a seismic data fault identification method based on SEU-Net. The SEU-Net network introduces SE blocks on the basis of the original U-Net network, enhancing the weights of effective channels through adaptive feature recalibration, and combines a multi-scale feature fusion strategy to improve the model’s ability to identify fault edges and minor faults. The experimental results show that compared with the original U-Net network, the SEU-Net network exhibits higher fault identification accuracy and robustness both in synthetic seismic data and original work area data. This research provides an efficient and automated solution for fault detection in seismic data and it holds certain theoretical value and practical application potential.

Keywords: fault identification; seismic data; SEU-Net; deep learning

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MDPI and ACS Style

Ren, W.; Chen, X.; Zhu, X.; Bao, D.; He, X.; Zhao, Y.; Zhao, M. Fault Identification of Seismic Data Based on SEU-Net Approach. Appl. Sci. 2025, 15, 10152. https://doi.org/10.3390/app151810152

AMA Style

Ren W, Chen X, Zhu X, Bao D, He X, Zhao Y, Zhao M. Fault Identification of Seismic Data Based on SEU-Net Approach. Applied Sciences. 2025; 15(18):10152. https://doi.org/10.3390/app151810152

Chicago/Turabian Style

Ren, Wenbo, Xuan Chen, Xiansheng Zhu, Dian Bao, Xinming He, Yan Zhao, and Ming Zhao. 2025. "Fault Identification of Seismic Data Based on SEU-Net Approach" Applied Sciences 15, no. 18: 10152. https://doi.org/10.3390/app151810152

APA Style

Ren, W., Chen, X., Zhu, X., Bao, D., He, X., Zhao, Y., & Zhao, M. (2025). Fault Identification of Seismic Data Based on SEU-Net Approach. Applied Sciences, 15(18), 10152. https://doi.org/10.3390/app151810152

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Fault Identification of Seismic Data Based on SEU-Net Approach

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