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Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area

Traditional fault interpretation mainly relies on human-machine interaction, which has low efficiency and high human uncertainty. Coherence attribute is sensitive to the discontinuity characteristics of seismic data and can effectively identify high grade faults. The coherence algorithm has undergone three innovations: cross-correlation (C1), similarity (C2), and eigenstructure (C3). In addition to coherence, attributes such as curvature, dip angle, and ant tracking have been proposed, and the likelihood attribute has developed rapidly in recent years, which can accurately reflect larger fault structures and has certain discrimination ability for small faults. However, due to the small moment and short extension length of low grade faults, they do not necessarily exhibit discontinuous characteristics at the fault location (especially for strike-slip faults), and the traditional attributes have not achieved good results in identifying small-scale faults. With the development of artificial intelligence algorithms in the field of target detection, advanced neural networks have proven to surpass traditional attributes in identifying faults from seismic data. This article takes the BD1 area of the Sichuan Basin as an example and combines fault enhancement interpretive processing such as dip scanning, structure-guided filtering, edge-preserving filtering, and frequeency filtering with artificial intelligence algorithms and transfer learning techniques for low grade fault identification research, forming a precise and reasonable artificial intelligence low grade fault identification technology process. The results show that the artificial intelligence algorithm using a large sample library can identify low grade faults that cannot be detected by traditional methods, and the fault detection results of artificial intelligence are superior to traditional attributes in terms of noise resistance, accuracy, and computational efficiency.

Low Grade Fault, Discontinuity Attribuet, Fault Enhancement, Artificial Intelligence, Large Sample Library, Transfer Learning

Chen Kang, Zhang Sheng, Zhang Xuan, Sun Desheng, Xu Xiang, et al. (2023). Artificial Intelligence Minor Fault Identification Technology and Its Application in BD1 Area. International Journal of Energy and Power Engineering, 12(4), 47-53.

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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