Volume texture extraction for 3D seismic visualization and interpretation

Visual inspection of poststack seismic image patterns is effective in recognizing large-scale seismic features; however, it is not effective in extracting quantitative information to visualize, detect, and map seismic features in an automatic and objective manner. Although conventional seismic attributes have significantly enhanced interpreters' ability to quantify seismic visualization and interpretation, very few attributes are published to characterize both intratrace and intertrace relationships of amplitudes from a three-dimensional (3D) perspective. These relationships are fundamental to the characterization and identification of certain geological features. Here, I present a volume texture extraction method to overcome these limitations. In a two-dimensional (2D) image domain where data samples are visualized by pixels (picture elements), a texture has been typically characterized based on a planar texel (textural element) using a gray level co-occurrence matrix. I extend the concepts to a 3D seismic domain, where reflection amplitudes are visualized by voxels (volume picture elements). By evaluating a voxel co-occurrence matrix (VCM) based on a cubic texel at each of the voxel locations, the algorithm extracts a plurality of volume textural attributes that are difficult to obtain using conventional seismic attribute extraction algorithms. Case studies indicate that the VCM texture extraction method helps visualize and detect major structural and stratigraphic features that are fundamental to robust seismic interpretation and successful hydrocarbon exploration.