Creating a quality map of a slate deposit using support vector machines

In this work, we create a quality map of a slate deposit, using the results of an investigation based on surface geology and continuous core borehole sampling. Once the quality of the slate and the location of the sampling points have been defined, different kinds of support vector machines (SVMs)—SVM classification (multiclass one-against-all), ordinal SVM and SVM regression—are used to draw up the quality map. The results are also compared with those for kriging.