An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure
Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault s...
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| Published in: | Energies (Basel) Vol. 15; no. 21; p. 8098 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Basel
MDPI AG
01.11.2022
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| ISSN: | 1996-1073, 1996-1073 |
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| Abstract | Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults. |
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| AbstractList | Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults. |
| Audience | Academic |
| Author | Zhang, Yujie Wang, Dongdong Ding, Renwei Yang, Jing Han, Tianjiao Zhao, Shuo Cai, Minghao Zhao, Lihong |
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| Cites_doi | 10.1109/EMBC46164.2021.9629671 10.1007/s11770-012-0315-7 10.1111/1755-6724.12307_12 10.1190/1.1817297 10.1007/978-3-642-40763-5_78 10.1190/1.1438880 10.1080/1463922X.2016.1166406 10.1016/j.neucom.2013.04.017 10.1190/geo2018-0646.1 10.1002/wics.1223 |
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| SubjectTerms | Accuracy Algorithms Analysis Artificial intelligence attention mechanism Deep learning Fault lines low-grade fault Methods Neural networks SE-UNet seismic data interpretation Semantics |
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| Title | An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure |
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