Optimising the placement of additional drill holes to enhanced mineral resource classification: a case study on a porphyry copper deposit

In this study, we compared outcomes of optimising the placement of five additional drill holes using three geostatistical cost functions (AKV, WAKV, and CV) and the Particle Swarm Optimisation algorithm (PSO). WAKV identified locations with higher average copper grades compared to AKV. Conversely, CV suggested sites with high kriging variance and copper grade variation. Initial holes, alongside those determined by each cost function, were used to classify mineral resources. Findings underscored the effectiveness of optimising drill hole placement based on cost functions in reducing uncertainty and improving mineral resource classification.本研究对比了采用三种地质统计成本函数（AKV、WAKV及CV）与粒子群优化算法（Particle Swarm Optimisation, PSO）对5个新增钻孔布设方案进行优化后的各项结果。相较于AKV方案，WAKV所确定的钻孔点位拥有更高的平均铜品位。与之相对，CV所推荐的点位则具备较高的克里金方差与铜品位变异性。我们将初始钻孔与各成本函数确定的钻孔一并用于矿产资源分类工作。本研究结果证实，基于成本函数优化钻孔布设，可有效降低不确定性并提升矿产资源分类的准确性。