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Zbl 1116.86009
Taboada, J.; Mat{\'\i}as, J.M.; Ordóñez, C.; Garc{\'\i}a, P.J.
Creating a quality map of a slate deposit using support vector machines. (English)
[J] J. Comput. Appl. Math. 204, No. 1, 84-94 (2007). ISSN 0377-0427

Summary: 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.\par The results obtained demonstrate that SVM regression and ordinal SVM are perfectly comparable to kriging and possess some additional advantages, namely, their interpretability and control of outliers in terms of the support vectors.\par Likewise, the benefits of using the covariogram as the kernel of the SVM are evaluated, with a view to incorporating the problem association structure in the feature space geometry. In our problem, this strategy not only improved our results but also implied substantial computational savings.

MSC 2000:: *86A32 Geostatistics

Keywords: Kriging; quality; slate; spatial statistics; support vector machines

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