Spatial contiguity analysis: a method for describing spatial structures of seismic data

We apply spatial contiguity analysis (SCA) to study spatial structures contained in seismic images. Compared to classical methods, such as principal component analysis (PCA), SCA is more efficient for multivariate description and spatial filtering of this kind of images. sent SCA according to geostatistic formalism defined by Matheron. A preliminary spatial analysis of initial variables is required. Made with the help of variogram curves, this permits to underline spatial properties of these variables and defines contiguity distance and direction to apply SCA. es of mathematical tools is defined. They allow quantifying the information held by initial variables and factorial components in terms of variance and spatial variability and exhibit data spatial structures on different scales. thod is applied to analyse a seismic data set. We compare PCA and SCA results. This data set gives us the opportunity to show the interest of preliminary spatial analysis of initial variables, and the effects of spatial direction and distance on the data decomposition in elementary structures.