Facies identification from well logs: A comparison of discriminant analysis and naïve Bayes classifier

The performance of a naïve Bayes classifier is compared with a well-established statistical classification approach, linear discriminant analysis, by considering core and log data from marine–eolian sediments. The results indicate that both methods perform adequately, and the Gaussian naïve Bayes classifier provides estimates as good as those based on the linear discriminant analysis for the given data set. Quadratic discriminant analysis, a more conventional Bayesian analysis, and kernel-based density estimation methods perform unexpectedly poor, probably because of overfitting. We conclude that the normal distribution is appropriate to fit the distribution of log readings in the present data, and the simplifications of naïve Bayes provide a robust, simple approach for facies identification.