Investigation of Grade Bias Due to Core Loss Using Bivariate Conditional Distribution

Abstract

Three-dimensional modeling of a mineral deposit was conducted based on the samples collected from the surficial and deep parts of the deposit. One of the factors that affect the quality of data is the recovery of cores acquired during exploration drilling. As the core recovery reduces, the grade in core or drilling mud increases. Core loss can introduce unpredictable errors and negative or positive bias into the mineral resources estimation. Investigating the grade–recovery relationship in a bivariate space helps to detect the grade bias. In an ideal state, there is no correlation between grade and recovery. In case the grade–recovery relationship possesses a negative or positive correlation, the grade bias is likely to be due to core loss. In practice, however, because there are many data in boreholes sample database, the grade–recovery relationship is not properly determined on a simple scatter plot of grade versus core recovery. Application of conditional distributions in a bivariate space provides the necessary tool for investigating the simultaneous variations of grade and core recovery. Therefore, having converted the grade and core recovery data into the normal space, the conditional grade distribution was determined for various recoveries, and then, the conditional grades expectations were determined and plotted against the recovery to investigate the grade–recovery relationship. Application of the proposed method in three different deposits showed that in low recoveries, due to waste loss, the grade values were overestimated, whereas in high recoveries, due to ore loss, they were underestimated.