Application of Sequential Gaussian Conditional Simulation into Underground Mine Design Under Grade Uncertainty

In mining projects, all uncertainties associated with the project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation as a reliable approach is the most widely used method to quantify risk analysis to overcome the drawbacks of estimation methods entire an ore-body. In this paper, all developed algorithms by numerous researchers for optimization of underground stope layout were reviewed. After that, a computer program called Stope Layout Optimizer 3D (SLO3D) was developed based on a new heuristic algorithm in order to incorporate the influence of grade variability in final stope layout. Utilizing Sequential Gaussian Conditional Simulation (SGS), 50 simulations and a kriging model were constructed for an underground copper vein deposit situated in the southwest of Iran and final stope layout was carried out separately. It was observed that geostatistical simulation can effectively cope with the weakness of kriging model. Final results showed that the frequency of economic value for all realizations varies between 6.7M$ and 30.7M$. This range of variation helps designers to make a better and lower risk decision under different conditions.