Geostatistical modeling of the geological uncertainty in an iron ore deposit |
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| Institution: | 1. Department of Mining Engineering, Faculty of Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran;2. Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran;3. Department of Mining Engineering, University of Chile, Santiago, Chile;4. Advanced Mining Technology Center, University of Chile, Santiago, Chile;5. CSIRO-Chile International Center of Excellence in Mining and Mineral Processing, Chile;6. School of Earth and Environmental Sciences, James Cook University, Townsville, Queensland 4811, Australia |
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| Abstract: | This paper addresses the problem of quantifying the joint uncertainty in the grades of elements of interest (iron, silica, manganese, phosphorus and alumina), loss on ignition, granulometry and rock types in an iron ore deposit. Sampling information is available from a set of exploration drill holes. The methodology considers the construction of multiple rock type outcomes by plurigaussian simulation, then outcomes of the quantitative variables (grades, loss on ignition and granulometry) are constructed by multigaussian joint simulation, accounting for geological domains specific to each quantitative variable as well as for a stoichiometric closure formula linking these variables. The outcomes are validated by checking the reproduction of the data distributions and of the data values at the drill hole locations, and their ability to measure the uncertainty at unsampled locations is assessed by leave-one-out cross validation.Both the plurigaussian and multigaussian models offer much flexibility to the practitioner to face up to the complexity of the variables being modeled, in particular: (1) the contact relationships between rock types, (2) the geological controls exerted by the rock types over the quantitative variables, and (3) the cross-correlations and stoichiometric closure linking the quantitative variables. In addition to this flexibility, the use of efficient simulation algorithms turns out to be essential for a successful application, due to the high number of variables, data and locations targeted for simulation. |
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| Keywords: | Geological heterogeneity Geological control Geological domaining Geostatistical simulation Stoichiometric closure |
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