Applicability of Categorical Simulation Methods for Assessment of Mine Plan Risk

Jewbali, A.; Perry, Ronald N.; Allen, Lawrence R.; Inglis, R.B.

doi:10.1007/978-3-319-69320-0_30

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The AusIMM volume, Advances in Applied Strategic Mine Planning (Dimitrakopoulos 2018), provides an overview of developments over the intervening decade since Osanloo et al (2008) and Newman et al (2010). One of its main topics is how conditional geostatistical simulations can be incorporated into mine planning (Osterholt and Dimitrakopoulos 2018;Benndorf and Dimitrakopoulos 2018;Menabde et al 2018;Dimitrakopoulos et al 2019;Khosrowshahi et al 2018;Godoy 2018;Dowd and Dare-Bryan 2018;Tavchandjian et al 2018;Jewbali and Dimitrakopoulos 2018;Froyland et al 2018;Jewbali et al 2018;Richmond 2018;Robins 2018). More recently, this has continued to be an important research topic.…”

Section: Stochastic Optimisation Applied To Mine Planningmentioning

confidence: 99%

Constructing Branching Trees of Geostatistical Simulations

Armstrong¹,

Valencia

Lagos

et al. 2021

Math Geosci

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This paper proposes the use of multi-stage stochastic programming with recourse for optimised strategic open-pit mine planning. The key innovations are, firstly, that a branching tree of geostatistical simulations is developed to take account of uncertainty in ore grades, and secondly, scenario reduction techniques are applied to keep the trees to a manageable size. Our example shows that different mine plans would be optimal for the downside case when the deposit turns out to be of lower grade than expected compared to when it is of higher grade than expected. Our approach further provides the probabilities of these outcomes; that is, the idea is to move toward adaptive mine planning rather than just producing a single mine plan.

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Section: Stochastic Optimisation Applied To Mine Planningmentioning

confidence: 99%

Constructing Branching Trees of Geostatistical Simulations

Armstrong¹,

Valencia

Lagos

et al. 2021

Math Geosci

View full text Add to dashboard Cite

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High-order simulation of geological domains and effects on stochastic long-term planning of mining complexes

Morales,

Dimitrakopoulos

2024

Mining Technology: Transactions of the Institutions of Mining a

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Stochastic long-term mine planning has evolved to account for different sources of uncertainty. Typically, the uncertainty and local variability of boundaries in geological domains have been overlooked by experts through their deterministic interpretation of available data. Categorical attributes are used to model geological domains, and their stochastic simulation accounts for the mentioned issues. The ability of two-points simulation methods to reproduce complex patterns or the requirement of a training image in multiple-points simulation methods has limited their implementation in mining environments. The high-order simulation of categorical attributes presents a mathematically consistent framework that overcomes these limitations by using high-order spatial statistics from sample data. The case study at a gold mining complex shows two stochastic mine plans based on two sets of geological realisations: geological domains in the first set are modelled using conventional wireframes, while, in the second, they are simulated through the high-order method. The resulting mine plans are substantially different; while both plans present a similar quantity of metal recovered and lifespan, risk profiles are up to 40% wider, and the expected NPV is 20% higher for the case of simulated geological domains, given the decrease of waste handling costs and the corresponding reduction in environmental footprint.

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Applicability of Categorical Simulation Methods for Assessment of Mine Plan Risk

Cited by 2 publications

References 3 publications

Constructing Branching Trees of Geostatistical Simulations

Constructing Branching Trees of Geostatistical Simulations

High-order simulation of geological domains and effects on stochastic long-term planning of mining complexes

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