Quantification of Uncertainty in Ore-Reserve Estimation: Applications to Chapada Copper Deposit, State of Goiás, Brazil

Abstract

Ordinary kriging (OK) has been used widely for ore-reserve estimation because of its superior characteristics in relation to other methods. One of these characteristics is related to the quantification of uncertainty by the kriging variance. However, the kriging variance does not recognize local data variability, which is an important issue in the process of ore-reserve estimation, when heterogeneous mineral deposits with richer and poorer parts are being evaluated. This paper proposes the use of interpolation variance as a reliable measure of local data variability and, therefore, adequate for ore-reserve classification. With a reliable measurement of data variability, local confidence can be calculated using the classical confidence interval around an estimate. Errors derived from local confidence then are used to assign classes according to a degree of certainty within some confidence level. Comparative tests using both OK variance and interpolation variance are carried out using exploration data from Chapada Copper Deposit, State of Goias, Brazil. Results show that the interpolation variance provides a better way to measure uncertainty and consequently to classify reserves.