Seismic image segmentation with multiple attributes

Image segmentation can automatically locate salt boundaries on seismic sections, streamlining an often time-consuming and tedious task when undertaken manually. However, using a single seismic attribute (usually amplitude) is sometimes insufficient to achieve an accurate segmentation result. Since any quantifiable measure may be employed as an attribute for segmentation, it is important to explore other possible attributes in order to develop a more robust segmentation algorithm. Specifically, dip variability and instantaneous frequency attributes show promise for providing unique information relevant to the segmentation problem. Possibilities for combining information from different attributes exist at several different stages of the segmenation process; the most promising methods incorporate an uncertainty measurement that can be easily calculated during segmentation. The ultimate result improves upon single-attribute segmentations by combining the most reliable information from different attributes along different portions of a salt boundary.