Model Selection for Mineral Resource Assessment Considering Geological and Grade Uncertainties: Application of Multiple-Point Geostatistics and a Cluster Analysis to an Iron Deposit

Abstract

The assessment of mineral resources requires the definition of a geological domain, and the quantitative results obtained should consider geological and grade uncertainties. However, when the available data are limited, it can be difficult to estimate the shape of an ore body owing to the spatial uncertainty. This study describes a methodology for defining geological domains using multiple-point geostatistics and selecting models for realizations using a cluster analysis. Then, the selected realizations were assessed for mineral resources using two-point geostatistics. Of the various multiple-point geostatistics algorithms, the single normal equation simulation was used to generate a realistic ore body, and various cluster algorithms, such as k-means and a density-based spatial clustering of applications with noise, were applied to select a model. Based on the outcomes, a mineral resource assessment that uses a sequential Gaussian simulation was performed, and a reasonable model that reflects the original data well was selected based on the grade statistics of the original data and mineral resource assessment per cluster. This study demonstrates that it is possible to define the ore domain and assess mineral resources while accounting for the uncertainty caused by a lack of spatial information.