<ul><li>Researchers propose a unified and interpretable deep learning inversion paradigm for analyzing airborne transient electromagnetic (ATEM) data.</li><li>Conventional methods for processing ATEM data have limitations in dealing with noise and achieving accurate inversion results.</li><li>The proposed approach involves disentangled representation learning to decompose noisy data into noise and signal factors.</li><li>The method demonstrates enhanced reliability and interpretability, leading to accurate reconstruction of subsurface electrical structure.</li></ul>

Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion

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