dc.creatorCuba, M.
dc.creatorLeuangthong, O.
dc.creatorOrtiz, J.
dc.date.accessioned2019-03-15T16:04:22Z
dc.date.available2019-03-15T16:04:22Z
dc.date.created2019-03-15T16:04:22Z
dc.date.issued2012
dc.identifierJournal of the Southern African Institute of Mining and Metallurgy, Volumen 112, Issue 11, 2018, Pages 971-983
dc.identifier22256253
dc.identifierhttps://repositorio.uchile.cl/handle/2250/165951
dc.description.abstractGeostatistical modelling aims at providing unbiased estimates of the grades of elements of economic interest in mining operations, and assessing the associated uncertainty in these resources and reserves. Conventional practice consists of using the data as errorfree values and performing the typical steps of data analysis - domaining, semivariogram analysis, and estimation/simulation. However, in many mature deposits, information comes from different drilling campaigns that were sometimes completed decades ago, when little or no quality assurance and quality control (QA/QC) procedures were available. Although this legacy data may have significant sampling errors, it provides valuable information and should be combined with more recent data that has been subject to strict QA/QC procedures. In this paper we show that ignoring the errors associated with sample data considerably underestimates the uncertainty (and consequently the economic risk) associated with a mining project. We also pr
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourceJournal of the Southern African Institute of Mining and Metallurgy
dc.subjectRealizations of simulated sample values
dc.subjectSampling error
dc.subjectUncertainty in final resources
dc.titleTransferring sampling errors into geostatistical modelling
dc.typeArtículo de revista


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