Abstract

As the cost in time and money of data from sensors and small-scale tests has reduced, additional challenges have arisen. Although many mine data sets are not big relative to those in other disciplines, they are complex in other ways, and becoming too large to manage efficiently on personal computers. In order to take advantage of the opportunity offered by increased data, a diverse team from Universidad de Chile and CSIRO Mineral Resources has developed a virtual geometallurgical laboratory to support the computational requirements linking data acquisition with data-driven business decisions. A particular focus to date has been the development of user-guided workflow ensembles to enable comparison between plausible geometallurgical hypotheses. A case study has been undertaken using publicly available data from the Rocklea Dome iron deposit in Western Australia. A hypothetical mining schedule was produced from geochemical and hyperspectral drill core/chips data via the following steps: data preprocessing, exploratory data analysis, geochemical interpolation, block modelling, ultimate open pit design and scheduling. The key outcome of this study was the integration of workflow stages through new plug-in methods or modification of existing software. Outcomes from this study will be described in the paper in terms of geological and metallurgical data quality, analytical software support and ongoing computing infrastructure requirements