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Optimizing Infill Drilling Decisions Using Multi-armed Bandits: Application in a Long-Term, Multi-element Stockpile

  • Rein Dirkx
  • Roussos Dimitrakopoulos
Chapter
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 19)

Abstract

Every mining operation faces a decision regarding additional drilling at some point during its lifetime. The two questions that always arise upon making this decision are whether more drilling is required and, if so, where the additional drill holes should be located. The method presented in this paper addresses both of these questions through an optimization in a multi-armed bandit (MAB) framework. The MAB optimizes for the best infill drilling pattern while taking geological uncertainty into account by using multiple conditional simulations for the deposit under consideration. MAB formulations are commonly used in many applications where decisions have to be made between different alternatives with stochastic outcomes, such as Internet advertising, clinical trials and others. The application of the proposed method to a long-term, multi-element stockpile, which is a part of a gold mining complex in Nevada, USA, demonstrates its practical aspects.

Keywords

Downside Risk Average Reward Cutoff Grade Kriging Variance Geological Uncertainty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Newmont Mining Corporation for providing us with the data necessary to conduct this research and the organizations that funded this research: the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant 239019 and the COSMO Mining Industry Consortium (AngloGold Ashanti, Barrick Gold, BHP Billiton, De Beers Canada, Kinross Gold, Newmont Mining and Vale) supporting the COSMO laboratory.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.COSMO – Stochastic Mine Planning Laboratory, Department of Mining and Materials EngineeringMcGill UniversityMontrealCanada

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