Use of a neural network to integrate geoscience information in the classification of mineral deposits and occurrences

A test of the ability of a probabilistic neural network to classify deposits into types based on a simple representation of mineralogy and six broad rock types is conducted here. The purpose is to examine whether this kind of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are identification of terranes permissive for deposit types and recognition that a few specific sites might be worth exploring extensively. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. Ore and alteration mineralogy and six rock types in 28 well-typed deposits were used to train the network. To reduce the number of minerals considered, analyzed data were restricted to minerals present in at least 50% of at least one deposit type. The training set was reduced to the presence or absence of 58 reported minerals and six generalized rock types from a total of 1005 deposits. Two kinds of independent tests are performed with 2751 deposits and occurrences from Nevada, U.S.A., that were not used in the training set. The first test is a deposit-type by deposit-type comparison of the neural network’s classification of 989 deposits with that of experts. Overall, the 53% agreement between the experts and the neural network is quite low compared to the 98% success reported in other studies. In the other kind of test, deposit types identified by the neural network are grouped and plotted into terranes determined by experts to be permissive for the grouped deposit types. Comparison of the spatial distribution of the neural network’s estimated deposit classes and permissive tracts determined by experts show that the probabilistic neural network is able to perform well at generalization. Classifying correctly over 98% of the sites in a large mineral database into the broad plutonrelated and epithermal classes suggests that the probabilistic neural network can efficiently identify terranes permissive for grouped deposit classes.