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Deep Learning (DL) inversion is a promising method for real timeinterpretation of logging while drilling (LWD) resistivity measurements forwell navigation applications. In this context, measurement noise maysignificantly affect inversion results. Existing publications examining theeffects of measurement noise on DL inversion results are scarce. We develop amethod to generate training data sets and construct DL architectures thatenhance the robustness of DL inversion methods in the presence of noisy LWDresistivity measurements. We use two synthetic resistivity models to test threeapproaches that explicitly consider the presence of noise: (1) adding noise tothe measurements in the training set, (2) augmenting the training set byreplicating it and adding varying noise realizations, and (3) adding a noiselayer in the DL architecture. Numerical results confirm that the threeapproaches produce a denoising effect, yielding better inversion results inboth predicted earth model and measurements compared not only to the basic DLinversion but also to traditional gradient based inversion results. Acombination of the second and third approaches delivers the best results. Theproposed methods can be readily generalized to multi dimensional DL inversion.

Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements