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Assessment of Recoverable Resource Uncertainty in Multivariate Deposits Through a Simple Machine Learning Technique Trained Using Geostatistical Simulations

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Abstract

Mine design, mine production planning and the economic evaluation of multivariate ore deposits are based on the mineral resources that are recoverable after applying cutoffs to the grades of selective mining units. Being able to reliably assess the recoverable resource uncertainty of more than one element is key for the economic evaluation of mining projects. Multivariate geostatistical models are difficult to fit and strongly influence the resources reported. The proposed approach avoids the delicate modeling step by directly estimating multivariate tonnages and their associated confidence intervals from a reduced set of features extracted from data. The predictive models are obtained by training with conditional simulations. For each case considered in the training phase, the coregionalization parameters are drawn randomly from within specified intervals and multiple realizations make it possible to obtain deposits conditional to the simulated data sets. Multiple linear regression training is carried out using input–output data to generate predictive models that relate the input variables calculated from the simulated data sets and the output variables (i.e., mean tonnage and tonnage quantiles) computed from the simulated deposits. The results from a synthetic bivariate case indicate excellent tonnage prediction and credible uncertainty quantification. A lateritic nickel deposit subject to a constraint on the maximum silica/magnesia ratio shows the applicability of this approach in a real-world context. The resulting tonnage surface and the associated uncertainty quantification provide an essential tool to help assess the economic value of the mine project.

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Availability of data and materials

The datasets generated and analysed during the current study are not publicly available due to confidentiality reasons but are available from the corresponding author on reasonable request.

Code availability

There is no available public code.

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Acknowledgments

This research was funded by the National Agency for Research and Development of Chile (ANID) through the Doctorado Becas Chile program (Grant Number 72180581), the National Research Council of Canada (Grant RGPIN-2015-06653), Quebec’s Merit Scholarship Program for Foreign Students (PBEEE, Grant Number 0000274857) and Polytechnique Montréal’s Doctoral Scholarship Program.

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Nadia Mery: Study conception, outlining and carrying out the experiments and analysis. Denis Marcotte: Study conception and supervise the research. The first draft was written by Nadia Mery, and subsequent versions were reviewed by Denis Marcotte. Both authors read and approved the final manuscript.

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Correspondence to Nadia Mery.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work presented in this manuscript.

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Mery, N., Marcotte, D. Assessment of Recoverable Resource Uncertainty in Multivariate Deposits Through a Simple Machine Learning Technique Trained Using Geostatistical Simulations. Nat Resour Res 31, 767–783 (2022). https://doi.org/10.1007/s11053-022-10028-9

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