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

被引:7
作者
Zhang, Yujie [1 ]
Wang, Dongdong [1 ]
Ding, Renwei [1 ]
Yang, Jing [1 ]
Zhao, Lihong [1 ]
Zhao, Shuo [1 ]
Cai, Minghao [1 ]
Han, Tianjiao [1 ]
机构
[1] Shandong Univ Sci & Technol, Coll Earth Sci & Engn, Qingdao 266590, Peoples R China
基金
中国国家自然科学基金;
关键词
seismic data interpretation; attention mechanism; SE-UNet; low-grade fault; DEEP; ALGORITHM;
D O I
10.3390/en15218098
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:17
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