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What Is Your Confidence In Your Simulated Confidence Interval?

Danny Kentwell
Thursday, October 18, 2018
First presented: 
AGCC 2018

Assessment of uncertainty in resource estimation is often quantified by deriving a confidence interval for a particular volume from a set of block simulations. The underlying assumption is that the set of parameters used for input to the simulation is fixed and that they are all correct. Just as kriging results and kriging quality are sensitive to the number of samples in the local search neighbourhood, so too are simulations that rely on local search neighbourhoods for their implementation. For example, fewer samples in the neighbourhood means a different kriging result and more importantly a larger kriging variance, this leads to a wider set of possible simulated values at each point/block and thus to a different set of confidence intervals for any given volume.

Just as with the kriging paradox of local accuracy vs global accuracy at cut offs above zero, we have to examine the sensitivity of the confidence intervals to search neighbourhood parameters and decide what set of search neighbourhood parameters are most appropriate.

Feature Author

Danny Kentwell

Danny Kentwell is a geostatistician with a background in geological modelling, mine planning and surveying. He has 25 years’ international experience with varied commodities including gold, copper, mineral sands, iron ore, nickel laterites, nickel sulphides and phosphate. Danny’s skills cover, 3D modelling, Resource estimation, open pit optimisation scheduling and design. His geostatistical expertise includes standard and recoverable resource estimation techniques such as uniform conditioning, indicator kriging and conditional simulation as well as multivariate estimation and simulation. As a geostatistician and engineer, he has an excellent understanding of the advantages and limitations of different resource estimation techniques, their resulting block grade, tonnage and value curves and their use in mine planning. Danny also has experience in applying geostatistical techniques to waste characterisation and determination of sampling adequacy from very small data sets.

Principal Consultant (Geostatistics)
MSc (Mathematics & Planning; Geostatistics), FAusIMM
SRK Melbourne
SRK Australia