We develop a novel physics-adaptive machine-learning (ML) inversion scheme showing optimal generalization capabilities for field data applications. We apply the physics-driven deep- learning inversion to a massive helicopter-borne transient electromagnetic (TEM) field data set. The objective is the ac- curate modeling of the near surface for enhancing the explora- tion of low-relief structures in a sand covered desertic area. Enhanced generalization of a neural network (NN) or other ML techniques is obtained from automatic physics-based adap- tive training using images of real-world data. Data reduction schemes, based on statistical sampling techniques, enable the use of small and fully informative data sets for accelerated train- ing. Adaptive learning is implemented via an iterative physics- driven data augmentation strategy. A deterministic inversion isregularized by a penalty term built from the difference between the inverted models and the models predicted by ML. The re- sulting inverted models and calculated responses are used for augmenting the ML training base. The automated procedure converges rapidly, producing a trained network model, which captures the general background data statistics as well as the characteristics of field-specific geophysical data as embedded in the augmented samples. The high-resolution ML inversions with acceptable levels of data misfit are obtained for large vol- umes of the field geophysical data. We test several different learning models, such as artificial NNs, convolutional NN (U-Net), and the Gaussian process, which provide stable and comparable results with network-specific characteristics. The developed scheme is successfully applied to micro-TEM data providing accurate near-surface seismic corrections for the ex- ploration of low-relief structures.
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