Geological modelling is a crucial step in mineral resource evaluation. The traditional approach to modelling the volumetric limits, explicit modelling, presents a series of limitations and disadvantages which makes it costly to assess the uncertainty in relation to the location of the limits between different domains in the mineral deposit. In many cases, the geological model can be a source of crucial uncertainty, for this reason, the uncertainty associated with the geological model must be assessed. This paper proposes a method for assessing geological model uncertainty by simulating the contacts between different domains in a mineral deposit in a hierarchical manner using signed distances. The proposed method was demonstrated in a case study conducted on a porphyry copper deposit. Models generated by the proposed method do not show much noise, as this method leads to continuous contacts between domains while the volume variation and contacts characteristics can be controlled by the parameters. Results are compared to sequential indicator simulation, a traditionally used technique to model geobodies and assess its uncertainty.
Evaluating mineral resources requires the prior delimitation of geologically homogeneous stationary domains. The knowledge about the ore genesis and geological processes involved are translated into three dimensional models, essential for planning the production and decision-making. The mineral industry usually considers grade uncertainty for resource evaluation; however, uncertainty related to the geological boundaries are often neglected. This uncertainty, related to the location of the boundary between distinct geological domains can be one of the major sources of uncertainty in a mineral project, and should be assessed due to its potential impact on the ore tonnage, and consequently, on enterprise profitability. This study aims at presenting three different methodologies capable of generating multiple geomodel realizations and thus, assessing uncertainty. A real dataset with high geological complexity is used to illustrate the methodology. The results are compared to a deterministic model used as a reference scenario.
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