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dc.contributor.authorHundelshaussen Rubio, Ricardo Josépt_BR
dc.contributor.authorCosta, Joao Felipe Coimbra Leitept_BR
dc.contributor.authorBassani, Marcel Antônio Arcaript_BR
dc.date.accessioned2016-07-23T02:17:54Zpt_BR
dc.date.issued2016pt_BR
dc.identifier.issn0370-4467pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/143926pt_BR
dc.description.abstractEstimation of some mineral deposits involves chemical species or a granulometric mass balance that constitute a closed constant sum (e.g., 100%). Data that add up to a constant are known as compositional data (CODA). Classical geostatistical estimation methods (e.g., kriging) are not satisfactory when CODA are used, since bias is expected when estimated mean block values are back-transformed to the original space. CODA methods use nonlinear transformations, and when the transformed data are interpolated, they cannot be returned directly to the space of the original data. If these averages are back-transformed using the inverse function, bias is generated. To avoid this bias, this article proposes geostatistical simulation of the isometric logratio ratio (ilr) transformations back-transforming point simulated values (instead of block estimations), with the averaging being postponed to the end of the process. The results show that, in addition to maintaining the mass balance and the correlations among the variables, the means (E-types) of the simulations satisfactorily reproduce the statistical characteristics of the grades without any sort of bias. A complete case study of a major bauxite deposit illustrates the methodology.en
dc.format.mimetypeapplication/pdf
dc.language.isoengpt_BR
dc.relation.ispartofRem: revista Escola de Minas. Ouro Preto, MG. Vol. 69, no. 2 (Apr./June 2016), p. 219-226pt_BR
dc.rightsOpen Accessen
dc.subjectCompositional dataen
dc.subjectSimulação geoestatísticapt_BR
dc.subjectIsometric transformations ratios (ilr)en
dc.subjectSimulationen
dc.subjectClosureen
dc.titleA geostatistical framework for estimating compositional data avoiding bias in back-transformationpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb000996957pt_BR
dc.type.originNacionalpt_BR


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