Comparison of kriging and neural networks with application to the exploitation of a slate mine

To carry out an efficient and effective exploitation of a slate mine, it is necessary to have detailed information about the production potential of the site. To assist us in estimating the quality of slate from a small set of drilling data within an unexploited portion of the mine, the following estimation techniques were applied: kriging, regularization networks ( RN), multilayer perceptron (MLP) networks, and radial basis function (RBF) networks. Our numerical results for the test holes show that the best results were obtained using an RN ( kriging) which takes into account the known anisotropy. Differing deposit configurations were obtained, depending on the method applied. Variations in the form of pockets were obtained when using a radial pattern with RBF, RN, and kriging models while a stratified pattern was obtained with the MLP model. Pockets are more suitable for a slate mine, which indicates that the selection of a technique should take account of the specific configuration of the deposit according to mineral type.