A novel algorithm for designing the layout of additional boreholes

A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

“…The probability of intersection between the target and the exploration network was calculated as a function of the target geometry and its relative orientation concerning the directional and dimensional properties of the exploration network. The target assumed ellipses because the shape of the surface projection of many natural resource targets can be approximated by an ellipse [9][10][11][12]. The optimum spacing of exploratory boreholes was evaluated by maximizing the expected gross drilling return (GDR) [13].…”

“…In the past decades, many other geostatistical objective functions have been proposed as an alternative to the kriging variance for optimal sampling design, such as the weighted kriging variance [26][27][28], interpolation variance [29][30][31], combined variance [31][32][33], conditional variance [34], information entropy [31,35], value of information [36], efficacy of information [37], GET (grade-estimation error-thickness) function [10,38], cross-validation error [39], interquartile range [40], probability interval widths [34,41], probability of classification error [42], probability of threshold exceedance [43], expected ore value [44], expected cost of classification errors [45], or increase of indicated and measured mineral resource categories [46]. These objective functions account for local ore grade variability, expected grade, expected productivity, expected profit, and/or reduction of uncertainty, unlike the kriging variance that only depends on the spatial correlation (variogram) and geometric configuration of the data.…”