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

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 exh...

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Bibliographic Details
Published in:Atmosphere 2024-06, Vol.15 (6), p.734
Main Authors: Tian, Yixing, Xie, Chengliang, Wang, Yun
Format: Article
Language:English
Subjects:
Algorithms
Ambient noise
Background noise
Cosmic rays
Data processing
Datasets
Deep learning
Dictionaries
Earth science
electromagnetic field
Electromagnetic fields
Fourier transforms
Geophysical data
Geophysics
Laboratories
Long short-term memory
LSTM
Methods
Morphology
neural network
Neural networks
Noise levels
Noise reduction
noise suppression
Observational learning
R&D
Reconstruction
Recurrent neural networks
Research & development
Signal processing
Signal to noise ratio
Theoretical analysis
Time series
underground laboratory
Wavelet transforms
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Summary: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.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos15060734