Seismic image segmentation relies upon attributes calculated from seismic data, but a single attribute (usually amplitude) is not always sufficient to produce an accurate result. Therefore, a combination of information from different attributes should lead to an improved segmentation outcome. This paper explores opportunities for combining attribute information at three different stages: before segmentation (by multiplying attribute volumes), after the eigenvector calculation (via a linear combination of individual eigenvectors), and after individual boundaries have been drawn (by using uncertainty calculations to extract the best elements of individual boundaries). Overall, a method that uses uncertainty calculations to determine weights for the eigenvector linear combination produces satisfactory results, while avoiding potential drawbacks of other methods. This method produces promising results when tested on field data in both two and three dimensions.
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