Adding value and confidence in Mineral Resource Estimation through exploratory data analysis: a case study

Cloete, Elsabe Maria
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The unexploited Gamsberg East deposit in the Northern Cape Province of South Africa, has been the subject of renewed interest as exploration target. This research study examined the available exploration drill hole data using a variety of data validation and analysis techniques. This was done to gain a sound understanding of the spatial and statistical characteristics of the data, which contributes to the confidence in the Mineral Resource Estimate. All information in the drill hole database was compiled and summarised into a validated dataset. This dataset was subjected to Exploratory Data Analysis, using a variety of graphical and statistical techniques to describe the distributions of grade within the deposit. An implicit geological model was created in Leapfrog Geo. The final model was used as the basis for variography and Mineral Resource Estimation through Ordinary Kriging. Exploratory Data Analysis resulted in the identification of the underlying grade probability distributions as CGLN. It was also found that outliers may represent a separate domain. A variety of methods was used in Leapfrog Geo and the outputs compared to produce a valid geological model for the deposit. Indicator Kriging and the refined model approach in Leapfrog Geo were used in an attempt to create subdomains. This did not yield the expected results, with subdomains still showing mixed populations. In the course of this work, the existence of a core and fringe zone was observed when displaying indicator values in 3D. These were modelled and used as domains in the Mineral Resource Estimate. Variography was conducted for the variable of interest within these domains and variograms showed geometric anisotropy, typical of base metal deposits. Inconclusive results from a Quantitative Kriging Neighbourhood Analysis resulted in the adaption of a kriging plan from a previous study over the deposit in question. The resultant Mineral Resource Estimate had low slope of regression indicating conditional bias. However, histograms and swath plots showed that the Mineral Resource Estimate fairly reproduced grade distributions within domains. Given the findings, it is recommended that simulation be considered to reduce conditional bias. Further work is also necessary to locate missing data and improve the Mineral Resource Estimate through unfolding. The use of simple statistical and graphical techniques in this study helped the practitioner achieve a thorough understanding of the data and its limitations. This increases the confidence in the final results of such a study
A research report submitted in partial fulfilment of the requirements for the degree of Master of Science in Mining Engineering to the Faculty of Engineering and the Built Environment, School of Mining Engineering, University of the Witwatersrand, Johannesburg, 2022