  This paper focuses on the denoising and enhancing of 3-D reflection seismic
data. We propose a pre-processing step based on a non linear diffusion
filtering leading to a better detection of seismic faults. The non linear
diffusion approaches are based on the definition of a partial differential
equation that allows us to simplify the images without blurring relevant
details or discontinuities. Computing the structure tensor which provides
information on the local orientation of the geological layers, we propose to
drive the diffusion along these layers using a new approach called SFPD
(Seismic Fault Preserving Diffusion). In SFPD, the eigenvalues of the tensor
are fixed according to a confidence measure that takes into account the
regularity of the local seismic structure. Results on both synthesized and real
3-D blocks show the efficiency of the proposed approach.
