Detection and extraction of fault surfaces in 3D seismic data

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 ͑LFE͒ 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-pixelthick, labeled fault surfaces that is readily usable for seismic interpretation.

Fault surfaces and fault throws from 3D seismic images

“…The computational cost of these recursive filters is independent of the spatial extent of their impulse responses, which may include well over 1000 samples in 3D images. This number represents the number of samples that contribute to the computation of fault likelihood for one orientation at one sample location in a 3D image (Cohen et al, 2006). With recursive smoothing filters I avoid this large factor in computational cost.…”

“…Much like Cohen et al (2006), I scan over multiple fault strikes and dips to determine the orientation that maximizes fault likelihood. In the 3D examples shown in this paper, this scan included N f = 26 fault strikes f and N q = 22 fault dips q , for a total of N f N q = 572 possible fault orientations.…”

Fault detection in seismic datasets using hough transform

“…For example, Gibsen et al [6] proposed a multistep workflow of fault surface detection, which first grouped the highlighted fault points in local planar patches and then merged these patches into large fault surfaces. Cohen et al [7] applied multiple directional filters to enhance the contrast of the discontinuity cube and then used skeletonization to extract the thinning fault surfaces. More recently, a class of fault detection techniques were proposed and are based on the concept that faults can be described as structured edges.…”

Automatic fault localization using the generalized Earth Mover's distance

“…Some example approaches include texture classification approaches [4], coherence-based methods [5], filtering-based methods [6], and greedy optimization algorithms to extract dominant paths in seismic images [7]. Most of these methods do not seem to work well in the presence of high levels of noise.…”