A Geostatistical Framework for Estimating Compositional Data Avoiding Bias in Back-transformation
Abstract
Estimation 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...
Paper Details
Title
A Geostatistical Framework for Estimating Compositional Data Avoiding Bias in Back-transformation
Published Date
Jun 1, 2016
Journal
Volume
69
Issue
2
Pages
219 - 226
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