<ul><li>The semi-airborne transient electromagnetic method (SATEM) is a useful surveying technique for challenging terrains.</li><li>DREMnet is an interpretable decoupled representation learning framework that enhances the denoising process of SATEM signals.</li><li>DREMnet disentangles data into content and context factors, leading to robust and interpretable denoising results in complex conditions.</li><li>Experimental results demonstrate that DREMnet outperforms existing techniques, improving the accuracy of identifying subsurface electrical structures.</li></ul>

DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal

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