Mineral resource modelling of variables with inequality constraints: a case study of an iron ore deposit
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Date
2021
Authors
Abulkhair, S.
Madani, N.
Editors
Musingwini, C.
Woodhall, M.
Woodhall, M.
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Conference paper
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Proceedings of the 40th International Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM, 2021), 2021 / Musingwini, C., Woodhall, M. (ed./s), pp.449-458
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Sultan Abulkhair, Nasser Madani
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Application of Computers and Operations Research in the Mineral Industry, (APCOM) (30 Aug 2021 - 1 Sep 2021 : virtual online)
Abstract
In multivariate geostatistics, it is common to have different types of complexities between variables of interest. In this context, an inequality constraint is an example of complex bivariate relationships. Unfortunately, traditional co-kriging and co-simulation algorithms cannot reproduce this type of bivariate complexity, leading to the overestimation of disturbing elements. This paper proposes a new algorithm based on a hierarchical sequential Gaussian co-simulation framework, integrated with inverse transform sampling, to model inequality constraints between variables. First, the proposed methodology's validity was evaluated by applying it to a real case study from an iron deposit, with an inequality constraint between iron and aluminum oxide. Then the simulated results were compared with a conventional hierarchical co-simulation algorithm to investigate the effect of inverse transform sampling on the quality of the co-simulation. The results showed that the proposed algorithm can reproduce an inequality constraint between variables.
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© The Authors Except where otherwise noted, this item's license is described as CC0 1.0 Universal