ABSTRACT Borehole microresistivity imagers produce high‐resolution images of the borehole wall, but incomplete tool coverage leaves vertical gaps that increase interpretation uncertainty. We present a fast, uncertainty aware inpainting approach based on conditional flow matching. The model operates on a two‐channel FMI representation and jointly inpaints both the static and dynamic normalized images within a single conditional generative flow. By fixing the observed measurements and repeating inference from different random noise initializations inside the missing region, we generate an ensemble of plausible completions and estimate pixelwise uncertainty from ensemble variability. Experiments on open access FMI data show coherent cross gap continuations and smooth gap boundaries in both channels, with inference speed suitable for real time and offline interpretation workflows.
Bittar et al. (Fri,) studied this question.