An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure

Yujie Zhang, Dongdong Wang, Renwei Ding (), Jing Yang, Lihong Zhao, Shuo Zhao, Minghao Cai and Tianjiao Han
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Yujie Zhang: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Dongdong Wang: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Renwei Ding: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jing Yang: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Lihong Zhao: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Shuo Zhao: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Minghao Cai: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Tianjiao Han: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Energies, 2022, vol. 15, issue 21, 1-17

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.

Keywords: seismic data interpretation; attention mechanism; SE-UNet; low-grade fault (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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