Identification and Suppression of Multicomponent Noise in Audio Magnetotelluric Data Based on Convolutional Block Attention Module
Liang Zhang (Guizhou University)
Guang Li (East China University of Technology)
Huang Chen (Chongqing University)
Jingtian Tang (Central South University)
Guanci Yang (Guizhou University)
Mingbiao Yu (Guizhou University)
Yong Hu (China University of Petroleum (East China), China University of Mining and Technology)
Jun Xu (Guizhou University)
Jing Sun (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
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.