APPLICATION OF SUPPORT VECTOR MACHINES IN COAL DEPOSIT BOUNDARY DECISION
AbstractIn order to make an accurate assessment of the tonnage and coal quality in coal mining, the boundaries of the coal deposits should clearly be defined. Uncertainty in determining the boundary should be taken into account while it has a vital role in geostatistical estimation and mine planning. In general, this uncertainty is evaluated by calculating probability of presence of coal at the given locations where data is not available. Indicator kriging which is non-linear spatial interpolation technique can be used in estimation of local uncertainty by the process of derivation of a local cumulative distribution function (cdf). Despite the simple theoretical basis for this method, there are limitations of indicator kriging such as variogram modelling which requires good command on geostatistics, order relations of results and their correction. In this study, support vector machines (SVM) are introduced as an alternative approach to indicator kriging estimation for defining the coal deposit boundary. The data in the field is converted into binary data and grid of unknown locations is estimated by the Gaussian Radial Basis Function kernel based classification SVM code which is implemented by the authors. The theoretical and practical bases for considerations and limitations of indicator kriging is discussed, along with proposed alternative through a case study.
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