Uncertainty assessment in an arbitrary volume without the use of geostatistical simulation.

2019

Alvaro Riquelme, Queen's University at Kingston ; Julian Ortiz, The Robert M. Buchan Department of Mining, Queen's Universty at Kingston

Ore bodies are variable in nature, and we can only access a few locations, through sampling, to characterize their properties. In mining, important economic decisions must be made, with fairly high levels of risk, based on this very limited information: the mining method, processing equipment and setting, plan and schedule. Several issues arise when the goal is to predict the performance of a specific process. Sample quality and spatial variability inject uncertainty in the final model. This uncertainty should be taken into consideration when making decisions, rather than basing these decisions on expected average values. This is critical when extreme values have large consequences in process performance. The state-of-the-art approach to this uncertainty quantification problem is the use of stochastic simulation, which generates multiple spatial models that must be processed to understand the effect of variability in the response variable. This process is computationally expensive and the results are hard to use and automate for decision-making. In this research, we present an approach to quantify the spatial uncertainty in an arbitrary volume without resorting to the use of stochastic simulation. The local uncertainty depends on the samples available and can be combined accounting for the spatial covariance of the variable under study. The performance of the proposed approach is compared with results obtained by stochastic simulation. Savings in computational complexity and performance are presented. A preliminary discussion on how to transfer the in situ uncertainty into the process is provided. Finally, synthetic and real case studies are developed to demonstrate the proposed approach and its potential benefits.
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