Recognising the impact of uncertainty in resource models
Description
The research aimed to test the variability of geological modelling due to the interpretation bias of the person building the model. Measuring this innate uncertainty in modelling allows stakeholders to have confidence in the models, knowing the level of risk involved and being able to account for it. New machine learning methods allow modelling scenarios to be tested in a fraction of the time of any previous modelling methods. Using MaptekTM DomainMCF, several different possible interpretations were produced with data from the Lisheen base-metal mine in Ireland. These were compared with previous modelling using MaptekTM VulcanTM techniques. Each model took about 10 minutes to generate, compared with one week in the past. Data from more than 100 mines was also processed using DomainMCF. The results at Lisheen showed significant variation. The models of the main mineralised body exhibited a volumetric variation of 12% between the most optimistic prediction of the geological domains and the most pessimistic interpretations. This is an important observation, as a variation of this magnitude will affect the resource statement. The alternative statement provides mine planners and potential investors with a quantitative assessment of the risk due to geological uncertainty.
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