Abstract

This paper addresses the problem of infill drill hole placement for mineral resource estimation and classification. The placement is considered optimal when it maximizes an objective function that accounts for ore grades, mineral resource classes, extraction priorities, and block volumes, where the grade and resource classes are defined on the basis of a set of geostatistical simulations. To expedite the identification of the optimal solution within a condensed timeframe, modifications to the random search (RS) algorithm are introduced, including a partition of the region targeted for drilling and the definition of a maximum distance to existing drill holes. The modified RS divides the study area into smaller areas and examines all these areas to find the optimal solution, in order to reduce the search time and to reach the best possible solution. This approach, furthermore, eliminates the impact of different random starting points and the risk of getting trapped in certain areas of the solution space. Also, the incorporation of a geometallurgical parameter (recovered metal) instead of the ore grade represents an innovation that signifies the consideration of mineral processing perspectives to optimize the drill hole placement. The proposed modified RS algorithm is applied to a dataset from an Iranian iron deposit consisting of 240 exploration drill holes, and resulted in 11% to 21% of the indicated resources being converted into measured resources after locating nine infill drill holes accounting for the iron grade and the recovered metal, respectively. The modified RS also compares favorably to other traditional optimization techniques. © 2025 by the authors.