Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards and the Fundamental Science on Radioactive Geology and Exploration Technology Laboratory;
Qingdao;
Xuzhou;
Guizhou University;
School of Resources and Geosciences;
Chongqing;
Delft University of Technology;
Delft;
Intelligent Systems Department;
School of Resources and Safety Engineering;
China University of Petroleum (East China);
Guiyang;
The Netherlands;
Key Laboratory of Advanced Manufacturing Technology;
School of Geosciences and Info-Physics;
Nanchang;
China|School of Geosciences;
Ministry of Education;
China;
China University of Mining and Technology;
East China University of Technology;
Chongqing University;
Central South University;
Changsha;
关键词：
Training;
Wavelet transforms;
Geology;
Noise reduction;
Time series analysis;
Neural networks;
Estimation;
期刊名称：
IEEE Transactions on Geoscience and Remote Sensing
i s s n：
0196-2892
年卷期：
2024 年
62 卷
页 码：
1-15
页 码：
摘 要：
Audio magnetotelluric (AMT) is commonly used in mineral resource exploration. However, the weak energy of AMT signals makes them susceptible to being overwhelmed by noise, leading to erroneous geophysical interpretations. In recent years, deep learning has been applied to AMT denoising and has shown better denoising performance compared to traditional methods. However, current deep learning denoising methods overlook the characteristics of AMT signals, resulting in reduced denoising accuracy. To enhance the denoising performance of deep learning by better matching the features of AMT signals, we propose a convolutional block attention module (CBAM)-based method for AMT denoising. This method focuses on the features of AMT signals and improves the process from three aspects: 1) in the establishment of the sample set, we adopt a multicomponent form based on the correlation of noise to enable the neural network to explore the potential connections among the components of AMT during the training process, thus constructing a stronger network mapping relationship; 2) in the construction of the neural network, we have introduced the CBAM structure into the residual blocks of the ResNet to enhance the network’s feature learning capability by focusing on the characteristics of noise; and 3) in the design of the denoising procedure, we adopt a process of identification before denoising to protect the noise-free data segments from being compromised during the denoising process. Finally, through synthetic, field data experiments, and comparative tests, we demonstrate that our proposed method achieves higher denoising accuracy than some traditional methods and conventional deep learning methods.
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