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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Vydané v:Energies (Basel) Ročník 15; číslo 21; s. 8098
Hlavní autori: Zhang, Yujie, Wang, Dongdong, Ding, Renwei, Yang, Jing, Zhao, Lihong, Zhao, Shuo, Cai, Minghao, Han, Tianjiao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.11.2022
Predmet:
Accuracy
Algorithms
Analysis
Artificial intelligence
attention mechanism
Deep learning
Fault lines
low-grade fault
Methods
Neural networks
SE-UNet
seismic data interpretation
Semantics
ISSN:1996-1073, 1996-1073
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en15218098