A simulation approach for improving the assessment of closure risks.

In the early stages of mine development the resource model is often based on sparse sample data. This can simplify the resource model and smooth the grade distribution and so misrepresent an orebody's mineability and associated grades. High density resource sampling can be used in the estimation of economic minerals and accompanying contaminants to minimise the risk of misrepresenting the mineralogical characteristics, and hence the economic value, of a reserve. A conditional simulation method for developing a range of mineralogical distribution models is introduced. It combines a geostatistical analysis of available data with automated mine design software to provide a rigorous method for assessing the confidence in the mineralogical characterisation of the ore reserve. It is based on simulating possible mine plans based on the statistical characteristics of the available resource sample data. These plans are then assessed against high resolution resource models generated using a sequential Gaussian simulation method. The method gives confidence intervals for important characteristics of the ore reserve estimate relative to the actual resource. These confidence intervals are an objective and transparent measure important for assessing closure risks and represent an improvement over common practice.
In the early stages of mine development the resource model is often based on sparse sample data. This can simplify the resource model and smooth the grade distribution and so misrepresent an orebody's mineability and associated grades. High density resource sampling can be used in the estimation of economic minerals and accompanying contaminants to minimise the risk of misrepresenting the mineralogical characteristics, and hence the economic value, of a reserve. A conditional simulation method for developing a range of mineralogical distribution models is introduced. It combines a geostatistical analysis of available data with automated mine design software to provide a rigorous method for assessing the confidence in the mineralogical characterisation of the ore reserve. It is based on simulating possible mine plans based on the statistical characteristics of the available resource sample data. These plans are then assessed against high resolution resource models generated using a sequential Gaussian simulation method. The method gives confidence intervals for important characteristics of the ore reserve estimate relative to the actual resource. These confidence intervals are an objective and transparent measure important for assessing closure risks and represent an improvement over common practice.