Reduction in uncertainty at the boundaries of ore deposits plays a critical role in mining projects. Such reductions can be made by choosing an appropriate objective function and using a suitable method for its optimization. In the literature, only a combined variance-based objective function can be found for modeling the problem of optimally locating additional drill holes to reduce uncertainty at the boundaries of mineralization. In this study, new objective functions based on interpolation variance (IV) and information entropy (IE) were developed and optimized through particle swarm optimization. The results of the proposed optimization methodology were compared with those of the combined variance-based objective function. Most of the proposed drill holes of the IV-based, IE-based and combined variance-based optimization methods are similar in many parts of the study area. However, IV-based and IE-based methods gave better results than the combined variance-based method. The proposed drill holes based on the IE-based objective function are better scattered over the area. This issue can be regarded as an advantage for the IE criterion both economically and technically.

减少矿床边界的不确定性在采矿项目中起着至关重要的作用。可以通过选择适当的目标函数并使用适当的方法对其进行优化来进行这种减少。在文献中，只能发现基于组合的基于方差的目标函数，用于对最佳定位其他钻孔以减少矿化边界不确定性的问题进行建模。在这项研究中，开发了基于插值方差（IV）和信息熵（IE）的新目标函数，并通过粒子群优化对其进行了优化。将所提出的优化方法的结果与基于组合方差的目标函数的结果进行了比较。大部分建议的基于IV的钻孔 在研究区域的许多地方，基于IE的优化方法和基于组合方差的优化方法都是相似的。但是，基于IV和基于IE的方法比基于组合方差的方法提供了更好的结果。基于基于IE的目标函数的建议钻孔在该区域分布得更好。从经济上和技术上，此问题都可以视为IE标准的一项优势。