Cokriging for optimal mineral resource estimates in mining operations.

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dc.citation.epage203en_ZA
dc.citation.issue3en_ZA
dc.citation.spage189en_ZA
dc.contributor.authorMinnitt, R.C.A.
dc.contributor.authorDeutsch, C.V.
dc.date.accessioned2016-11-03T09:59:16Z
dc.date.available2016-11-03T09:59:16Z
dc.date.issued2014
dc.descriptionThis paper arose from the Citation Course in Geostatistics presented by the second author in Johannesburg, South Africa in August 2011.en_ZA
dc.description.abstractCokriging uses a sparsely sampled, but accurate and precise primary dataset, together with a more abundant secondary data-set, for example grades in a polymetallic orebody, containing both error and bias, to provide improved results compared to estimation with the primary data alone, as well as filtering the error and mitigating the effects of conditional bias. The method described here may also be applied in polymetallic orebodies and in other cases where the primary and secondary data could be collocated, and one of the data-sets need not be biased, unreliable, etc. An artificially created reference data-set of 512 lognormally distributed precious metal grades sampled at 25×25 m intervals constitutes the primary data-set. A secondary data-set on a 10×10 m grid comprising 3200 samples drawn from the reference data-set includes 30 per cent error and 1.5 multiplicative bias on each measurement. The primary and secondary non-collocated data-sets are statistically described and compared to the reference data-set. Variograms based on the primary data-set are modelled and used in the kriging of 10×10 m blocks using the 25×25 m and 50×50 m data grids for comparison against the results of the cokriged estimation. A linear model of coregionalization (LMC) is established using the primary and secondary data-sets and cokriging using both data-sets is shown to be a significant improvement over kriging with the primary data-set alone. The effects of the error and bias are filtered and removed during the cokriging estimation procedure. Thus cokriging using the more abundant secondary data, even though it contains error and bias, significantly improves the estimation of recoverable reserves.en_ZA
dc.description.librarianMvdH2016en_ZA
dc.description.urlhttp://www.saimm.co.za/publications/journal-papersen_ZA
dc.identifier.citationMinnitt, R.C.A. and Deutsch, C.V. 2014. Cokriging for optimal mineral resource estimates in mining operations. The Journal of The Southern African Institute of Mining and Metallurgy 114(3), pp. 189-203. <http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2014000300008&lng=en&nrm=iso>en_ZA
dc.identifier.issn2225-6253
dc.identifier.urihttp://hdl.handle.net/10539/21373
dc.journal.titleJournal of the Southern African Institute of Mining and Metallurgy.en_ZA
dc.journal.volume114en_ZA
dc.language.isoenen_ZA
dc.publisherThe Southern African Institute of Mining and Metallurgy.en_ZA
dc.rightsThis Journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.en_ZA
dc.subjectCo-Krigingen_ZA
dc.subjectLinear model of coregionalizationen_ZA
dc.subjectOptimal resource estimatesen_ZA
dc.subjectOrdinary krigingen_ZA
dc.subjectPrimary data-seten_ZA
dc.subjectSecondary data-seten_ZA
dc.subjectOre depositsen_ZA
dc.titleCokriging for optimal mineral resource estimates in mining operations.en_ZA
dc.typeArticleen_ZA
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