Fault identification and enhancement using residual U-Net: Application to field seismic data

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

Acknowledgments

None.

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.

Author contributions

Conceptualization: All authors

Formal analysis: Jianhua Wang, Cong Niu, Yandong Wang, Yun Ling, Di Wang

Investigation: Jianhua Wang, Cong Niu, Yandong Wang, Yun Ling, Di Wang

Methodology: Jianhua Wang, Cong Niu, Yandong Wang, Yun Ling, Di Wang

Writing-original draft: Jianhua Wang, Cong Niu, Yandong Wang

Writing-review & editing: All authors

Availability of data

All data generated and analyzed during this study are included in this published article.

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