ABSTRACT
Underground mine planning aims at determining the amount and sequence of extraction of the mineral resources in order to maximize the deposit profit. It involves multiple sources of information which add risk to the developed plan. Among the risks that can affect the financial return of a mining company, those associated with the geology of the deposit stand out; more specifically the amount of metal contained within the deposits or parts of it.
This risk is mainly due to the complexity of the geological process of the mineral deposits allied to the sparse drilling spacing available used for constructing deterministic models to determine the tonnages and contents of the variable of interest.
This paper aims at develop a methodology to quantify the risk related to the prediction of metal contained in a stope. The risk will be measured based on mine planning constructed through probabilistic models based on geostatistical simulations of the zinc content. Additionally, density is calculated and volume designed for each scenario simulated.
Each designed stope will have its metal content predicted by each simulated model after volume correction using the regression error. Multiple simulations allow to calculate the probability of metal contained at each stope. Based on these results, a risk indicator is proposed to flag the associated risk with the metal contained value in each stope. From this indicator, a rank from low to high risk zones can be taken into account during mining scheduling.
