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

• Published in: Atmosphere Vol. 15; no. 6; p. 734
• Main Authors: Tian, Yixing, Xie, Chengliang, Wang, Yun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• 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; France
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos15060734

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.