Abstract
Application of pattern recognition techniques to reflection seismic data is difficult for several reasons. The amount of available training data is limited by the degree of well control in the area and may not be sufficient. In contrast, seismic data sets are often extremely large, necessitating the use of the smallest possible feature set to allow quick and efficient processing. In this paper, a method to generate synthetic training data is described, which alleviates the problem of insufficient training data. A means is provided for injecting a priori geologic knowledge into the classifier, including well logs. Finally, a feature evaluation algorithm using a performance metric related to the Bayes probability of error is outlined and applied to the training data to identify effective feature sets.
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