Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion

תקציר מקורי באנגליתarXiv:2503.22214v2 Announce Type: replace Abstract: 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, a unified and interpretable deep learning inversion paradigm based on disentangled representatio