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Modelling a complex gold deposit with multiple-point statistics
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DOI：10.1016/j.oregeorev.2021.104427.png)
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This paper presents an innovative approach to the geological modelling and stochastic estimation of a Mineral Resource in a structurally complex gold deposit. A Direct Sampling multiple-point statistics algorithm is adopted to produce stochastic models of the lithology and gold grade distributions while using sparse geological and grade data. A multivariate modelling framework shows potential for modelling finely intercalated folded li-thologies, which are problematic with classical methods. The method performs well with sparse data or even in the absence of data, provided auxiliary variables are available. Geological input is limited to an elementary training image and structural measurements of the lithological contacts to create a representative potential field of ductile deformation. A new modelling framework is adopted where a portion of the 'true' model is reserved to be used as a training image, while the remaining part serves as a validation tool prior to arriving at a satisfactory ensemble of realisations. This ensemble is carried forward as a stochastic model for the poorly informed part of the deposit. This approach lends itself to the adaptation of modern developments in artificial intelligence and machine learning. The geologist's involvement is confined to incorporating an understanding of the deposit and its genesis into the explicit 'prior' model.
Keyword:
Multiple-point statistics (MPS)
Direct Sampling algorithm (DS)
Training image
Non-stationarity
Uncertainty modelling
Geological modelling
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