In order to aid the interpretation of seismic data, we present a new method for the denoising and fusion of multiple 3D registered blocks of the same area of subsoil. We propose to combine low-level fusion and diffusion processes through the use of a unique model based on partial differential equations (PDE). The denoising process is driven by the seismic fault enhancing diffusion equation. Meanwhile, relevant information (as seismic faults) is injected in the fused blocks by an inverse diffusion process. One of the advantages of such an original approach is to improve the quality of the results in case of noisy inputs, frequently occurring in seismic unprocessed data. Finally, two examples will demonstrate the efficiency of our method on synthetic and real seismic data.

This paper presents a method that estimates curvature of both 2-D and 3-D oriented patterns at different scales even if the structures are very close. Assuming that a structure is locally defined by an implicit isointensity contour, the curvature is obtained by a direct computation stemming from the differential geometry. The method is applied to curvature estimation of seismic image for geological interpretation.

The present study is devoted to the statistical analysis of edges in still monochrome TV pictures. The visual information carried by the edges is especially important both for image interpretation and for subjective image quality evaluation. Statistical knowledge on edges is helpful to improve image coding techniques significantly as well as processing techniques for scene analysis. After an introduction on nonstationary local statistical models, we describe the parameters of edges and the methods used to measure them. Statistical data collected on these parameters are then presented. The data concern orientation, edge length, edge width, runlength between edges and edge slope probability distributions as well as the measure of orientation continuity along an edge and the relative frequencies of edge pixels and contrasted isolated pixels.