Geometallurgy – A Geologist’s Approach

Source: Harraden, C.L., McNulty, B.A., Gregory, M.J., & Lang, J.R., 2013. Shortwave Infrared Spectral Analysis of Hydrothermal Alteration Associated with the Pebble Porphyry Copper-Gold-Molybdenum Deposit, Iliamna, Alaska, USA. Economic Geology 108 (3), p. 483-494.

From a geologist’s point of view geological models form the starting point of successful geometallurgical studies. Most importantly, geological models consist of domains with similar characteristics, whether that be rock type or hydrothermal alteration type, that provide a framework for the geometallurgical characterization of an ore deposit.

Presentations and written contributions on the expanding area of geometallurgy are often focused on the definition of the term “geometallurgy”, with discussion concentrated on the aims, benefits, and importance of geometallurgical studies.   However, the planning, designing and, more importantly, the execution of geometallurgical studies are of considerably more importance.  Many of the current approaches to geometallurgy involve the analysis of bulk samples to produce data sets that combine mineralogy and whole-rock major and trace element geochemistry with automated core logging and rock scanning technology to quantify rock texture. In such studies, sample selection is driven by geostatistical modeling and requires a large number of samples to satisfy the statistics.

An economic geologist’s approach to geometallurgy, particularly at the exploration stage of a project, can be very different to the methodologies above.  The in-situ study of metal deportment is the key input to geometallurgical modeling in a geological approach.  By this we mean using carefully selected samples prepared as thin sections and polished blocks to understand the associations of the economic metals of interest with other minerals by integrating traditional petrographic studies with advanced SEM-based mineral mapping.  This approach has significant advantages in that the distribution of metals is understood both spatially and temporally, i.e., in terms of the paragenetic position of the metals within the evolution of the mineralizing system.  Using this method, the major controls on metal deportment, such as alteration assemblage, rock type, or structural fabric, can be clearly identified, and a basic geometallurgical model can be constructed based on a combination of drill core logging with assay data or other large and easily accessible (or obtainable) data types such as short-wave infrared spectral data.  At the exploration stage, this type of basic geometallurgical model can provide major insights into future mineral processing issues, and as the project advances such a model can then form the basis for large scale, detail geometallurgical sampling and analytical programs such as those mentioned above.

Source: Gregory, M.J., Lang, J.R., Gilbert, S. & Hoal, K.O. 2013. Geometallurgy of the Pebble Porphyry Copper-Gold-Molybdenum Deposit, Alaska: Implications for Gold Distribution and Paragenesis. Economic Geology 108 (3), p. 463-482.

This approach has been used at the Pebble porphyry Cu-Au-Mo deposit in Alaska, driven by the geology and metallurgy teams. The key finding from this work is that the host mineral for gold varies with changes in the alteration assemblages, and that each association requires a different metallurgical approach to optimize metal recovery.  Three-dimensional modeling of the alteration domains provides a direct input to the metallurgists, allowing volumes of each geometallurgical material type to be estimated, and this is then used to guide metallurgical test work sample selection.  The metallurgical test work results feed back into the geometallurgical study, validating the results and allowing for continuous improvement in the models.

This simple and effective methodology for geometallurgical characterization using the in-situ approach not only has the ability to contribute to improved process design but also identifies some of the key geological controls on metal deportment at the deposit scale, which helps develop improved ore deposit models.  Both of these outcomes have economic benefits by providing increased metal recoveries and by contributing to improved exploration program design.