Identification and Suppression of Multicomponent Noise in Audio Magnetotelluric Data Based on Convolutional Block Attention Module

Identification and Suppression of Multicomponent Noise in Audio Magnetotelluric Data Based on Convolutional Block Attention Module

Zhang, Liang (Guizhou University)
Li, Guang (East China University of Technology)
Chen, Huang (Chongqing University)
Tang, Jingtian (Central South University)
Yang, Guanci (Guizhou University)
Yu, Mingbiao (Guizhou University)
Hu, Yong (China University of Mining and Technology; China University of Petroleum (East China))
Xu, Jun (Guizhou University)
Sun, J. (TU Delft Pattern Recognition and Bioinformatics)

2024

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.

Convolutional Block Attention Module (CBAM)
ResNet
Audio Magnetotelluric (AMT)
Denoising

http://resolver.tudelft.nl/uuid:fc625a4f-c5bc-4ba2-8a25-1663f031444a

https://doi.org/10.1109/TGRS.2024.3361942

2024-08-05

1558-0644

IEEE Transactions on Geoscience and Remote Sensing, 62

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2024 Liang Zhang, Guang Li, Huang Chen, Jingtian Tang, Guanci Yang, Mingbiao Yu, Yong Hu, Jun Xu, J. Sun