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An approach for optimising final pit limits for a highly variable and geologically complex gold deposit is presented which uses a heuristic pit optimiser to manage the effect of geological uncertainty in the pit shell with multiple uncertainty rated solutions. The models are created using real data (from exploration drill holes and economic production data) from a complex folded gold vein deposit in Africa. The uncertainty rated pit shells follow the mean-variance criterion to approximate the efficient frontier for final pit limits. Stochastic dominance rules are then used in a risk management framework to further eliminate sub-optimal solutions along the efficient frontier. This results in a smaller set of final pit shells that could be further analysed for production scheduling. The original solutions are analysed for changes in the mining limits and two regions are targeted as potential regions for further exploration., 27 refs., An approach for optimising final pit limits for a highly variable and geologically complex gold deposit is presented which uses a heuristic pit optimiser to manage the effect of geological uncertainty in the pit shell with multiple uncertainty rated solutions. The models are created using real data (from exploration drill holes and economic production data) from a complex folded gold vein deposit in Africa. The uncertainty rated pit shells follow the mean-variance criterion to approximate the efficient frontier for final pit limits. Stochastic dominance rules are then used in a risk management framework to further eliminate sub-optimal solutions along the efficient frontier. This results in a smaller set of final pit shells that could be further analysed for production scheduling. The original solutions are analysed for changes in the mining limits and two regions are targeted as potential regions for further exploration.

Scenario-level uncertainty is a representation of large-scale differences encountered when considering ultimate mining pits optimised over various subsets of stochastic realisations. Accounting for scenario-level uncertainty allows mine plans to be assessed against several probabilistic subsurface interpretations. Scenario-level uncertainty is found through clustering stochastic geostatistical realisations and optimising final pits across the realisations in each cluster. Where these pits are geometrically different, plans for each scenario differ. Knowing these differences allows the value of each planning alternative to be assessed based on the value and relative probability of each scenario. These results also facilitate the use of formal decision-making frameworks for planning, leading to optimisation that better represents the full statistical characterisation of the deposit. An example utilising this approach evaluates both infrastructure placement and drilling programme requirements. In both cases, scenario-level uncertainty improves initial plans by limiting opportunity costs and identifying additional potential. (Authors.), Scenario-level uncertainty is a representation of large-scale differences encountered when considering ultimate mining pits optimised over various subsets of stochastic realisations. Accounting for scenario-level uncertainty allows mine plans to be assessed against several probabilistic subsurface interpretations. Scenario-level uncertainty is found through clustering stochastic geostatistical realisations and optimising final pits across the realisations in each cluster. Where these pits are geometrically different, plans for each scenario differ. Knowing these differences allows the value of each planning alternative to be assessed based on the value and relative probability of each scenario. These results also facilitate the use of formal decision-making frameworks for planning, leading to optimisation that better represents the full statistical characterisation of the deposit. An example utilising this approach evaluates both infrastructure placement and drilling programme requirements. In both cases, scenario-level uncertainty improves initial plans by limiting opportunity costs and identifying additional potential. (Authors.)