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

作者:

Highlights:

摘要

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.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.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.

论文关键词:07.05.Kf,07.05.Mh,Kriging,Quality,Slate,Spatial statistics,Support vector machines

论文评审过程:Received 15 July 2005, Revised 1 December 2005, Available online 5 June 2006.

论文官网地址:https://doi.org/10.1016/j.cam.2006.04.030

Back to Top