2025/03/28 by Shuang Wang, Ming Guo, Wang, Shuang +11 · 2 citations
Earth and Planetary Sciences · #Geophysical and Geoelectrical Methods #Seismic Waves and Analysis #Earthquake Detection and Analysis
paper · pdf · doi:10.1109/tgrs.2026.3680901
The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas. However, the acquired signals are often contaminated by complex noise, which can compromise the accuracy of subsequent inversion interpretations. Traditional denoising techniques primarily rely on parameter selection strategies, which are insufficient for processing field data in noisy environments. With the advent of deep learning, various neural networks have been employed for SATEM signal denoising. However, existing deep learning methods typically use single-mapping learning approaches that struggle to effectively separate signal from noise. These methods capture only partial information and lack interpretability. To overcome these limitations, we propose an interpretable decoupled representation learning framework, termed DREMnet, that disentangles data into content and context factors, enabling robust and interpretable denoising in complex conditions. To address the limitations of convolutional neural network (CNN) and transformer architectures, we utilize the receptance weighted key value (RWKV) architecture for data processing and introduce the contextual weighted key value (Co-WKV) mechanism, which allows unidirectional weighted key value (WKV) to perform bidirectional signal modeling. Our proposed Cover Embedding technique retains the strong local perception of convolutional networks through stacked embedding. Experimental results on test datasets demonstrate that the DREMnet method outperforms existing techniques, with processed field data that more accurately reflects the theoretical signal, offering improved identification of subsurface electrical structures.