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

Zhang, Liang; Li, Guang; Chen, Huang; Tang, Jingtian; Yang, Guanci; Yu, Mingbiao; Hu, Yong; Xu, Jun; Sun, Jing

doi:10.1109/tgrs.2024.3361942

Cited by 4 publications

(1 citation statement)

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“…Most typical deep learning-based noise suppression methods are designed for single-channel data. Zhang et al [47] proposed a multi-channel approach using CNNs and applied the method in audio magnetotelluric data processing, although longer-period data remain ambiguous. The efficacy of supervised learning-based methods depends on available training datasets that encompass the principal signal and noise morphological features.…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations

Tian,

Xie,

Wang

2024

Atmosphere

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Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefiting from a low background noise level. Enhancing strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio, is both valuable and feasible. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at the surface, to achieve noise suppression through signal reconstructions. Both the theoretical analysis and the practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

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Section: Introductionmentioning

confidence: 99%