Deep-learning-based airborne transient electromagnetic inversion providing the depth of investigation

Abstract

We develop an integrated workflow that uses deep-learning (DL)-based approaches for processing and inverting airborne transient electromagnetic (ATEM) data. Our novel workflow automates these preprocessing steps and enables real-time inversion in the field. Thus, we develop an entire inversion workflow using three DL networks that cover all steps from preprocessing to imaging. The preprocessing DL network performs interpolation to discard data that are severely noise contaminated and suppress the effects of noise in a late-time channel. We use an inversion DL network and a depth of investigation (DOI) network to generate images of subsurface resistivities exclusively within the DOI range where reliable predictions can be made. To optimize the inversion process, our approach focuses on designing the inversion DL network to simultaneously minimize data misfit and model misfit. By addressing these two aspects, we ensure a more robust outcome in the final resistivity images. The practical applicability of the workflow is verified by comparing the imaging results of the field data with those of conventional inversion and geologic interpretation. Each workflow is nearly automatic and very fast; we expect that our workflow will contribute to the development of real-time imaging software for the ATEM survey, which expands the applications of the ATEM survey in various fields.