New outlier top-cut method for mineral resource estimation via 3D hot spot analysis of borehole data

Abstract

Three-dimensional (3D) analysis of borehole data is very important for effective mineral exploration. It can be used not only to understand the geological structure of the underground, but to estimate the amount of the resource. In the mining industry, the geostatistical interpolation, such as kriging, is widely used to predict the value of a whole section using this borehole data. In order to obtain reasonable prediction results, it is firstly necessary to verify assay and geological databases. In addition, if the assayed grade data deviates significantly from the average value, it is necessary to perform the prediction including the outlier top-cut because it may excessively affect the predicted value. However, the existing top-cut methods of determining a specific threshold value may cause an error by excluding significant data. In this study, to minimize the loss of such data, we developed a 3D hot spot analysis technique to analyze statistically significant outliers. In addition, it was applied to borehole data analysis of the Au deposit. As a result, we confirmed that the proposed method can mitigate the overestimation or underestimation that might occur when applying the existing methods.