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Simulation of geo-domains accounting for chronology and contact relationships: application to the Río Blanco copper deposit

Abstract

The plurigaussian model is increasingly used for simulating geo-domains and quantifying geological uncertainty in the subsurface. However, because they rely on the truncation of only two Gaussian random fields, the current implementations of this model are often restricted in the number of geo-domains that can be simulated and in their contact relationships. A solution to overcome these restrictions is to increase the number of underlying Gaussian random fields. Such an approach yields a very flexible model, able to reproduce the contact relationships between geo-domains in agreement with their chronology (i.e., such that younger geo-domains cross-cut the older ones), as well as the geo-domain proportions and spatial correlation structure. The proposed approach is applied to a dataset from the Río Blanco copper deposit in the Chilean Central Andes, in which it is of interest to simulate the layout of seven rock units (andesite, granitoid, tourmaline breccia, monolithic breccia, magmatic breccia, porphyry and pipe). The results are used to map the probabilities of occurrence of the rock units and to identify the sectors where the interpreted rock model is uncertain.

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Acknowledgments

This research was funded by the Chilean Commission for Scientific and Technological Research, through Project CONICYT/FONDECYT/REGULAR/No. 1130085. The authors acknowledge the support from Claudio Martínez from Codelco-Chile (Andina Division), who provided the dataset used in this work, as well as the comments by Grégoire Mariethoz and another anonymous reviewer, who helped to improve the manuscript.

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Correspondence to Xavier Emery.

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Madani, N., Emery, X. Simulation of geo-domains accounting for chronology and contact relationships: application to the Río Blanco copper deposit. Stoch Environ Res Risk Assess 29, 2173–2191 (2015). https://doi.org/10.1007/s00477-014-0997-x

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Keywords

  • Geological uncertainty
  • Geological heterogeneity
  • Domaining
  • Plurigaussian simulation
  • Hierarchical modeling