Volume attribute computation is an accepted part of mainstream interpretation workflows. Perhaps counter-intuitively, attribute generation is powerful because it creates data sets that show only a subset of the information available in the original seismic. By reducing the information content, it is easier to focus on those aspects of the seismic response that help differentiate particular aspects of the imaged geology. Seismic attributes often measure properties of the seismic signal and the trace-to-trace variation in seismic signal that have an opaque relationship to rock properties. Therefore, interpretation of such attributes is generally based on identification of geologically reasonable scenarios. This can be greatly facilitated by examining multiple attributes simultaneously in a spatially coregistered manner—to either increase the differentiation between features of interest or to the show the relationship between different types of seismic response. A powerful way to achieve this is the use of color-blending techniques (Henderson et al., 2007) (Figure 1). Color blending effectively illuminates the geology, but consequently creates a complex image in which the information is hard to access other than visually. Accurate extraction of the information perceived within a color blend is one of the interpretation challenges associated with the improvements in visualization technology.