Results on seismic and borehole datasets provided by {\it Shell Research} using two different types of classifier, a Gaussian based classifier and neural network will be shown which verify that the suggested method works well.
Knowing what features to calculate for a query is a problem. Using as many features as possible not only results in a computational costly system but can also give worse results then if a sub-set of those features had been used. Several traditional metho ds for carefully selecting the set of features will be compared which significantly reduce the feature set size without a corresponding degradation in performance.
A novel method of selecting inputs and hidden units for the the neural network will then be introduced. It is demonstrated that this algorithm out-performs these more traditional feature selection methods.