Considering in situ grade variability during mining sequence

Mine planning and scheduling is based on using a geological model for the deposit. Blocks are used to discretize the deposit into mineable volumes where grade values are assigned. Traditional estimation techniques such as kriging or inverse distance weighting are frequently used for this task where an interpolated value is calculated within a discrete volume. This group of block grades is the input for sequence optimizers, which base their decision on the economical value of a given block. Ultimately, the economical value of a block depends on its grade, making mine sequencing sensitive to all estimated values. If one looks to a block not as a single grade but as a set of equally possible grades, its economical value will change as different grades can be assigned to it. Stochastic simulation is known to provide the tools to assess grade variability, which makes it possible to generate equally probable values in a block. Different grade block models can thus be built and multiple sequencing alternatives are observed by running the optimizer for these distinct inputs. The results demonstrate that the methodology is adequate for risk assessment and proves to be useful for mining project sensitivity analyses. Differences in the project net present value showed that there is little difference between the two simulation methodologies (sequential Gaussian simulation and sequential indicator simulation) applied. The process is demonstrated using a Brazilian kaolin deposit. Keywords: geostatistical simulation, mine planning, sequencing, risk assessment, open pit optimization.