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Fault segmentation in seismic data is still a challenge, even with the advent of Deep Learning and neural architectures like U-Net ( Ronneberger et al., 2015 ) and Transformers ( Vaswani et al., 2017 ). While end-to-end solutions abstract the need to extract useful information from the data, real-world applications might benefit from hybrid, pipeline approaches. In this work, we introduce a two-step method for the fault segmentation task: an initial model produces a segmentation mask with sufficient precision so that a posterior robust estimator like RANSAC ( Fischler and Bolles, 1981 ) fits plane geometries to cover faults. Combining the initial mask with the fitted planes seems to increase the Recall, Fl -Score, and IoU (Intersection over Union) by sacrificing some precision.