Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion
This work addresses the challenge of processing complex, high-noise field data in geophysics, offering a more reliable and interpretable solution for subsurface imaging, though it is incremental in advancing deep learning applications in this domain.
The authors tackled the problem of extracting geoelectric structural information from noisy airborne transient electromagnetic data by proposing a unified and interpretable deep learning inversion paradigm, which directly uses noisy data to accurately reconstruct subsurface electrical structures and improves lateral structural resolution compared to traditional methods.
The extraction of geoelectric structural information from airborne transient electromagnetic(ATEM)data primarily involves data processing and inversion. Conventional methods rely on empirical parameter selection, making it difficult to process complex field data with high noise levels. Additionally, inversion computations are time consuming and often suffer from multiple local minima. Existing deep learning-based approaches separate the data processing steps, where independently trained denoising networks struggle to ensure the reliability of subsequent inversions. Moreover, end to end networks lack interpretability. To address these issues, we propose a unified and interpretable deep learning inversion paradigm based on disentangled representation learning. The network explicitly decomposes noisy data into noise and signal factors, completing the entire data processing workflow based on the signal factors while incorporating physical information for guidance. This approach enhances the network's reliability and interpretability. The inversion results on field data demonstrate that our method can directly use noisy data to accurately reconstruct the subsurface electrical structure. Furthermore, it effectively processes data severely affected by environmental noise, which traditional methods struggle with, yielding improved lateral structural resolution.