The application of macro co-kriging and compound lognormal theory to long range grade forecasts for the carbon leader reef

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dc.contributor.authorChamberlain, Vaughan Andrew
dc.date.accessioned2017-04-19T08:12:05Z
dc.date.available2017-04-19T08:12:05Z
dc.date.issued1997
dc.descriptionA project report submitted to the Faculty of Engineering, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg, 1997.en_ZA
dc.description.abstractDue to the extreme costs of establishing new shaft systems in Witwatersrand gold mines it is essential that the resource estimation is optimised, The result of poor Of sub-optimal estimation could be catastrophic even.to the largest of mining companies. This project examines the application of Compound Lognormal Distribution theory and shows the advantages of this distribution model over more traditional models, for the Carbon Leader Reef. The incorporation of information from mined out areas of a deposit in resource estimation is demonstrated. The critical role played by accurate geological modelling is highlighted. The process of Macro Co-Kriging in conjunction with Compound Lognormal Theory is discussed in detail and is shown to be a more accurate estimation technique than traditional techniques using Lognormal theory. Finally the use of the Macro Co-kriged limits are shown to be useful in the classification of Mineral Resources.en_ZA
dc.description.librarianAC2017en_ZA
dc.format.extentOnline resource (55 leaves)
dc.identifier.citationChamberlain, Vaughan Andrew (1997) The application of macro co-kriging and compound lognormal theory to long range grade forecasts for the carbon leader reef, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/22398>
dc.identifier.urihttp://hdl.handle.net/10539/22398
dc.language.isoenen_ZA
dc.subject.lcshMine valuation
dc.subject.lcshMine shafts--Economic aspects
dc.titleThe application of macro co-kriging and compound lognormal theory to long range grade forecasts for the carbon leader reefen_ZA
dc.typeThesisen_ZA
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