Abstract

In a survey of Landmark, Paradigm, Schlumberger, and TerraSpark commercial products, the median number of parameters per volume process (such as attributes, or smoothing processes) was two (Figure 1a), with some volume processes having as many as 18 parameters. Each parameter used to control a volume process represents an added dimension to the parameter space of that process. With many parameters, evaluating that parameter space to find the best combination of parameters for a given data set can be quite time-consuming. Selecting the best parameter values can be daunting for an experienced user and an effective “barrier to entry” for a potential new user, causing them to not even try and instead move on to another type of technique.

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