Novel approaches to geometallurgy using geochemistry - same data, new tricks!

Geometallurgy is a cross-disciplinary activity with the primary aim of characterising ore in terms of critical processing performance, in a quantified and spatially constrained manner. A key objective of geometallurgy is the economic optimisation of mining through reduction of technical risk. Geological variability lies at the heart of geometallurgy and defining this variability can present many challenges. For example, mineralogy is a fundamental processing characteristic for a given rock mass throughout the mining chain, so understanding the mineralogy of the bulk rock, including both the gangue and ore component (in this case Cu-bearing minerals), is critical when making predictions on processing characteristics. This objective can be achieved through advancement in techniques for determining quantitative mineralogy, through mathematical modelling and machine learning, using commonly available chemical assay and geological logging data.

Calculating modal mineralogy from chemical assay provides a fast and cost effective alternative method to estimating bulk mineralogy. To demonstrate this we provide an example of the calculation of complex mineralogy for the Productora Cu-Au-Mo deposit using 133,963 inductively coupled plasma atomic emission spectroscopy (ICP-AES) multi-element analyses and a training set of 625 quantitative X-ray diffraction (QXRD) analyses. These data sets are used to constrain the ore and alteration mineralogy of the hypogene, supergene and transitional ore at Productora.

We also present a new approach to defining oxide, transitional and sulphide material on a sample by sample basis at the same deposit in order to improve the spatial definition of variable Cu speciation. A simple Cu species classification scheme based on sequential leach data and S% has been devised to classify oxide, transitional and sulphide Cu, and also to account for non-recoverable Cu, i.e. nonsulphide Cu which is insoluble in weak acids. Through machine learning, a proxy for this Cu species classification scheme was developed based on: depth, Ca %, Cu %, Fe %, K %, Mn ppm, S % and Ln(Cu/S) plus logged regolith class, thus allowing classification to be extended to areas of the deposit, where no sequential leach data are available.

Results from these two novel approaches to predicting mineralogy and Cu species suggest that geochemistry and geochemical proxies can be used successfully, resulting in a high density of deposit wide data and increased ore body knowledge at low cost. The new data has been used to generate deposit-wide 3D models for mineralogy, including total quartz + feldspar % and total pyrite %, and Cu species classification that can be used in geometallurgy studies.