Statistical recognition of seismic reflection patterns is a useful aid to seismic data interpretation. The seismic texture classification classifies and recognizes seismic data into a limited number of categories each characterized by a distinct style of reflectivity. The basis in the recognition is a set of reference windows (2D) or blocks (3D) representing all major types of reflectivity from the sections of interest.

The seismic data is analysed as 2-D windows each modelled as a Markov random field, where the probability distribution of small patterns in the window is used to characterize the seismic texture. In 3-D the seismic block is represented as a collection of 2-D windows, where the average probability distribution of small patterns in the windows characterize the seismic texture of the block. The recognition of the seismic texture uses a linear projection where the seismic texture is projected onto categories of reflectivity. This makes the method computationally efficient.

The information obtained by the seismic texture classification is most easily seen in the vertical display as illustrated on small extractions from 2-D and 3-D offshore dataset.

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