A Monte Carlo Simulation-based algorithm for updating Combined Variance cost function in optimally locating additional drillholes

Abstract

ABSTRACT Optimally locating additional boreholes in the boundaries of ore deposits is an important problem in mining projects. To solve this problem, combined variance has been proposed as a cost function to be minimized. An issue about combined variance is its dependence on the value of variable at unknown location. This value is achieved through a round-based algorithm that estimates the probability of occurrence of ore and rounds it to the nearest integer without considering all scenarios. This study aims to consider all scenarios using a Monte Carlo simulation-based algorithm. This approach uses ordinary cokriging to estimate the probability of occurrence of ore at unknown locations. Then, the estimated probabilities are entered into a Monte Carlo simulation procedure to generate various realizations. The method was applied in the Chadormalu ore deposit to propose ten additional boreholes. The round-based algorithm proposed additional drill holes in the middle of ore-intersected and non-intersected initial drill holes. However, the Monte Carlo-based algorithm is sensitive to the thickness such that in boundaries with thicker ore, the additional boreholes are suggested farther away from the ore-intersected drill holes and closer to the non-intersected ones and vice versa.