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# Conclusions

Seismic image segmentation using a single attribute is not always sufficient to produce an accurate salt boundary calculation, so the use of other attributes such as dip variability is often necessary. By combining information from different attributes, we hope to incorporate the most reliable information from each attribute into a single, improved segmentation result. While opportunities for such combinations exist at several stages of the segmentation process, the most promising method in 2D involves a linear combination of eigenvectors from individual attributes, weighted according to uncertainties derived from each eigenvector. In the examples here, this approach successfully incorporates useful information from two different attributes, while avoiding potential pitfalls of other methods. This method may be extended to three dimensions with the assumption that weight values are constant in the crossline direction; in the 3D example shown here, such an approach yields an improved eigenvector and accurate salt interface pick on a 3D seismic cube. While the constant-weights assumption is part of an early and somewhat primitive approach, the results here hold promise for the success of more sophisticated 3D image segmentations schemes.

Next: Acknowledgments Up: Halpert and Clapp: Attribute Previous: Attribute combinations in 3D