Multi-element (ME) datasets provide comprehensive geochemical signatures of an orebody and are commonly used to gain insight into the mineralogy, lithology, alteration patterns and to identify target-pathfinders. However, little effort is made in using these data to explain comminution or recovery characteristics. This paper describes an agglomerative hierarchical clustering approach applied to ME data from the Tropicana Gold Mine, Australia, and investigates the relationship between the resultant classes and run-of-mine comminution and recovery parameters. First, it is demonstrated how an industry scale ME dataset is prepared for clustering. The preparation consists of verifying the absence of interlaboratory and intralaboratory bias between measurements, centred log-ratio transformation (clr), normalisation and principal component analysis (PCA). Afterwards, the first case study indicate that the clustering separation is primarily driven by geochemical differences caused by major rock-forming mineral signatures (felsic vs mafic, alteration vs no alteration, chert or quartz lithologies, unmineralised vs mineralised material). This case study separates the ME dataset into five unmineralised and two Au-mineralised material classes. The second case study continues with the two identified mineralised material classes and further separates these samples into five new classes. These classes are explored geochemically and by using the spatial context (within domains) better matched with metallurgical test results. It is found that domain-related material class proportions assist in interpreting different processing proxies such as the Equotip hardness (Leeb), Bond Work index (BWi), Axb, and processing recovery and reagent consumption. Knowledge of the processing parameters per domain and class composition can be used to infer such characteristics in the absence of standard metallurgical tests. This new approach of gaining insights into comminution and recovery parameters through geochemical analysis demonstrates the benefit of the conceptualised material fingerprinting concept.
- Agglomerative hierarchical clustering
- Comminution and recovery parameters
- Four-acid digestive multi-element ICP data
- Mineral processing
- Tropicana Gold Mine