Employing support vector machine, statistical and geostatistical methods to design the detailed exploration grid of Khomein-Robat Pb-Zn deposit


Mining Engineering Department, College of Geosciences Engineering, Arak University of Technology, Arak, Iran


In current research, primary classification of Khomein-Robat-Arregijeh Pb-Zn deposit has been ‎carried out first, using support vector machine (SVM) method. To achieve this goal, 548 ‎available multivariate data point comprising 337 induced polarization (IP) and resistivity (Rs) ‎geophysical data, 211 rock type and Pb-Zn assay data from outcrops, trenches and test pits, ‎three variables IP, Rs and rock type were considered as predictive variables as well as Pb-Zn ‎assay as target variable. Afterward through designing and training an SVM model, on the basis ‎of three cut off grade 1.5, 2 and 3 percent, the deposit was classified to two classes: high-grade ‎zone or anomaly (above the cut off grade) and low-grade zone or background (lower than cut ‎off grade). Then exploratory boreholes scattered in the region, were drilled in the locations ‎known as high-grade zone as well as anomalies of the geophysical pseudo-sections. In the next, ‎using all exploration information, designing the detailed exploration grid of the deposit was ‎performed through statistical and geostatistical methods. A rectangular grid with dimensions of ‎‎36*35m was obtained by analytical method based on classical statistics. Employing geostatistical ‎method and 3-D variography for Pb-Zn assays of the boreholes using SGEMS software ‎concluded a square grid with dimensions of 55*55m. According to the results of the research, ‎semi-deep exploration activities are suggested at the nodes of the new exploration grid, designed ‎by geostatistical method due to more accuracy of the method.‎


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