Jeong, Won-KiWhitaker, RossDobin, MarkRaghu Machiraju and Torsten Moeller2014-01-292014-01-2920063-905673-41-X1727-8376https://doi.org/10.2312/VG/VG06/111-118This paper presents a 3D, volumetric, seismic fault detection system that relies on a novel set of nonlinear filters combined with a GPU (Graphics Processing Unit) implementation, which makes interactive nonlinear, volumetric processing feasible. The method uses a 3D structure tensor to robustly measure seismic orientations. These tensors guide an anisotropic diffusion, which reduces noise in the data while enhancing the fault discontinuity and coherency along seismic strata. A fault-likelihood volume is computed using a directional variance measure, and 3D fault voxels are then extracted through a non-maximal-suppression method. We also show how the proposed algorithms are efficiently implemented with a GPU programming model, and compare this to a CPU implementation to show the benefits of using the GPU for this computationally demanding problem.Categories and Subject Descriptors (according to ACM CCS): I.4.0 [Image Processing and Computer Vision]: General; I.3.8 [Computer Graphics]: Applications