Constructing Branching Trees of Geostatistical Simulations

Margaret Armstrong, Juan Valencia, Guido Lagos, Xavier Emery

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)711-743
Number of pages33
JournalMathematical Geosciences
Issue number4
StatePublished - May 2022


  • Adaptive optimisation
  • Geological uncertainty
  • Mine planning
  • Multi-stage programming with recourse
  • Scenario reduction


Dive into the research topics of 'Constructing Branching Trees of Geostatistical Simulations'. Together they form a unique fingerprint.

Cite this