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Fault identification and enhancement using residual U-Net: Application to field seismic data
Jianhua Wang1* ,
Cong Niu1 ,
Yandong Wang1 ,
Yun Ling1 ,
Di Wang1 ,
Xiuping Jiang2,3,4 ,
Chenshuo Yuan2
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1
CNOOC Research Institute Co., Ltd., Beijing,
China
2
College of Marine Geo-sciences, Ocean University of China, Qingdao, Shandong,
China
3
Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong,
China
4
Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education, Ocean University of China, Qingdao, Shandong,
China
Submitted: 3 September 2025 | Revised: 17 November 2025 | Accepted: 27 November 2025 | Published: 20 January 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract
Fault identification is a critical step in seismic data interpretation. Traditional fault identification methods rely heavily on manual interpretation, which is inefficient and significantly influenced by subjective factors. This paper proposes a fault identification algorithm based on a Residual U-Net–curvelet hybrid framework. By introducing residual learning strategies and applying batch normalization and skip connection techniques, the generalization ability and convergence speed of the network are enhanced, thereby improving the accuracy and efficiency of fault identification. Results from field data processing demonstrate that this method achieves high identification accuracy under complex geological structures and low signal-to-noise ratio conditions, providing reliable fault identification results for efficient seismic data interpretation.
Keywords
Fault identification and enhancement
Deep learning
Residual U-Net
Random noise suppression
Funding
This research is jointly funded by the National Natural Science Foundation of China (U23B20158) and the Major Science and Technology Project of China National Offshore Oil Corporation (CNOOC) during the “14th Five- Year Plan” (KJGG2022-0104).
Conflict of interest
The authors declare no conflict of interest.
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