... the relevance of attributes to the geological nature of salt domes and the number of attributes used for classification. The algorithm works by combining the attributes from the Gray Level Co-occurrence Matrix (GLCM) and those from the Gradient of Texture (GoT) attributes with a dictionary-based learning...

... imaging projects for salt interpretation. We demonstrate that providing additional texture constraints to existing layer-tracking algorithms can improve their performance for automatically tracking TOS boundaries. boundary glcm seg denver 2014 probability distribution amplitude sediment...

... as suggested by a manual interpretation. visualization boundary matrix texture segmentation contour reservoir characterization covariance matrix classification glcm variance reflector berthelot detection probability image interpretation grey level artificial intelligence timeslice...

...-defining the number of clusters and mapping them against continuous 1D, 2D and 3D colorbars, which the interpreter then visually clusters. We generate SOM clusters based on the wavelet shape, on the spectral component and the GLCM attributes of the Red-Fork formation and correlate the results...

... seismic signal character. Recently, several workers (West et al., 2002; Gao, 2003; Chopra and Alexseev, 2005) have extended this technique to 3D seismic data through the use of the gray-level cooccurrence matrix (GLCM). GLCM allows the recognition of patterns significantly more complex than simple edges...

... not be standardized. More recently, the use of statistical measures to classify seismic textures using grey-level co-occurrence matrices (GLCMs) (West et al, 2002, Gao, 2003) has been introduced. The idea behind texture analysis of surface seismic data is to mathematically describe the distribution of pixel values...