Multi-element geochemistry for bulk mineral characterization of hydrothermal alteration associations in porphyry copper deposits and applications to predictive geo-metallurgical modeling

In the present study we discuss the value of multi-element geochemistry as a tool for semi-quantitative to quantitative bulk mineral characterization in geological and geo-metallurgical modeling of porphyry copper type deposits, with an emphasis on hydrothermal alteration associations, types and intensities. In mine production of porphyry copper deposits much effort is placed on ore grade and geological modeling, applied for predictive mineral processing behavior, from blasting, grinding, leaching or flotation, down to waste disposal and environmental management. High confidence geological models are commonly based on geological mapping of drill core and surface/ underground exposures, combined with discrete mineral characterization (e.g., petrography, QEMSCAN®, spectroscopy, etc.). Unfortunately these are expensive and applied to limited numbers of samples, thus lacking sufficient representation and not suitable for geo-statistical modeling. In addition, geological mapping always presents uncertainty, based mostly on qualitative estimates. The combination of information usually provides geological models that are hard to combine with geo-metallurgical data, much less with mineral processing predictive behavior models. The advent of multi-element geochemistry, at low cost, has allowed employment of such tools massively, yet little use is given to the data, usually focused on elements of economic interest (main ore and credits) and elements of negative impact to concentrates and/or process. Geochemistry reflects the mineral composition of rocks, thus allowing characterization of lithology, alteration types and intensities, and mineralization. As examples of such applications we present generic classification techniques which were developed on various porphyry copper deposits of northern Chile, these based on multi-element geochemistry, allowing for semi-quantitative to quantitative high resolution modeling of key aspects of lithology, hydrothermal alteration and mineralization, and which may be cross-referenced to metallurgical test samples and by that, to geo-metallurgical properties of rocks and modeling. In addition, as data is quantitative and continuous, modelling of gangue can benefit from geo-statistical modeling.