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Abstract We propose an efficient method for detecting and extracting fault surfaces in 3D-seismic volumes. The seismic data are transformed into a volume of local-fault-extraction (LFE) estimates that represents the likelihood that a given point lies on a fault surface. We partition the fault surfaces into relatively small linear portions, which are identified by analyzing tilted and rotated subvolumes throughout the region of interest. Directional filtering and thresholding further enhance the seismic discontinuities that are attributable to fault surfaces. Subsequently, the volume of LFE estimates is skeletonized, and individual fault surfaces are extracted and labeled in the order of decreasing size. The ultimate result obtained by the proposed procedure provides a visual and semantic representation of a set of well-defined, cleanly separated, one-pixel-thick, labeled fault surfaces that is readily usable for seismic interpretation.
Cohen et al. (Sun,) studied this question.