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Testing new estimation techniques – rank and replace: a stockwork case study

Danny Kentwell, Dave Finn
Friday, September 22, 2017
First presented: 
AusIMM 10th International Mining Geology Conference, 20-22 September 2017
Published paper
Experience has shown that the E-Type multiple indicator kriging (MIK) estimation technique often works well for resource estimation in stockwork mineralisation that contains a mixture of high and low-grade mineralisation types that are difficult or impossible to separate spatially. E-Type MIK, like ordinary kriging (OK) is a smoothing estimator and where data is sparse can result in estimates of grade tonnage curves that do not reflect the true block variability at selective mining unit (SMU) scale. The rank and replace (RR) estimation methodology is designed to reproduce both local block accuracy and true block variability at SMU scale and has shown good results when used with highly skewed unimodal diffusive style mineralisation. This paper examines the use of RR with mosaic style mineralisation typically found in stockworks via a case study using real data from Newcrest’s Telfer West Dome deposit. Resource level estimation results for ordinary kriging (OK), E-Type MIK and RR are compared to close spaced grade control models as the reference reality.
Results show that stationarity requirements and declustering can severely impact on the usefulness of the rank and replace technique and that it should only be used where trends are not too strong and data are relatively uniformly spaced. This does not rule out its application to stockwork or mosaic style deposits. However, in this case, the presence of trends and different data densities requires the non-stationary domain to be further broken down into sub domains for the RR technique to be effective.

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 Africa