The Cowal Gold Operation in central New South Wales (Australia) has recently commenced production from the underground GRE46 gold deposit. GRE46 is expected to contribute a significant portion of the site's future gold production, which was previously derived exclusively from the E42 open pit. This new, more variable ore-type introduces additional complexity to the processing circuit. Deposit-scale quantification of the mineralogical variability that underpins rock physio-chemical behaviour during the extraction cycle is therefore a critical aspect of orebody knowledge that reduces uncertainty and technical risk. In this study we demonstrate a workflow to develop, refine and subsequently verify a spatially constrained three-dimensional calculated mineralogy model for GRE46 that can be used to correlate with comminution characteristics that may influence the processing performance. Here we present the results of two element-to-mineral conversion models using common multi-element geochemical assays with increasing levels of geological input, and compare the calculated mineralogy to mineralogy data determined by X-ray diffraction. The initial model adopts nominal end member and stoichiometric mineral compositions, while the refined model incorporates domain-specific mineral solid solution compositions for chlorite and white mica measured by laser ablation inductively-coupled plasma mass spectrometry. The accuracy of predicted white mica and chlorite content is improved substantially between the initial and refined models relative to the measured X-ray diffraction data (white mica R 2 0.61 initial vs 0.90 refined ; chlorite R 2 0.3 initial vs 0.67 refined ). This work highlights the importance of appropriate geochemical domaining and mineral solid solution chemistry on the predictive accuracy for several bulk minerals at GRE46.
High-purity quartz (HPQ) is a critical material for advanced technological applications and is essential to the global transition towards net-zero emissions. The stringent purity requirements of HPQ necessitate specialist analytical methods capable of detecting trace-level contaminants and characterising impurity distribution within the quartz matrix. This review provides a comprehensive overview of analytical techniques employed for HPQ assessment, categorised into field-portable screening tools, bulk chemical analyses, mineralogical and structural characterisation, in situ microanalysis, and advanced specialised methods. We systematically compare these approaches, discussing their principles, capabilities, and limitations, and emphasise the importance of integrating bulk and microanalytical strategies to accurately evaluate impurity deportment and suitability for high-end applications. To support decision making, this review compares indicative analytical costs and outlines staged characterisation approaches that align analytical effort with project objectives and maturity. By addressing gaps in previous literature, this synthesis offers guidance for selecting and sequencing analytical workflows tailored to HPQ characterisation and downstream processing requirements.
The manganite (manganese ore) deposit at Grandhome, City of Aberdeen, has an anomalously high uranium content and negligible thorium content. The very low Th/U ratio is characteristic of a supergene environment, in this case during Devonian exposure of basement rocks at the Caledonian unconformity. The uranium exceeds 100 ppm in mineralized whole rock, and up to 360 ppm in manganite ore. In manganite uranium resides in the yttrium arsenate mineral chernovite-(Y) as a substituent along with rare earth elements. Arsenic levels are an order of magnitude above background. The occurrence exemplifies low-temperature mineralization on orogenic exposure surfaces.
This paper presents an overview of current practises in incorporating humus (O-horizon in the soil profile) sampling in greenfield mineral exploration in glaciated terrain with examples from Canada and Fennoscandia. The use of humus for mineral exploration is based on the premise that plants can accumulate metals of interest from the substrate and that plant debris decays to form humus (the uppermost part of the soil). Humus can, therefore, potentially provide insight into the substrate's geochemistry. If mineralization is present in the vicinity, then its geochemical signature may be reflected within the decomposed plant matter (humus). This paper provides a detailed overview of how to incorporate humus into regional greenfield mineral exploration surveys in glaciated terrain, including the theory, field methods such as site selection and sample collection, quality assurance and quality control protocols, sample pre-processing including the optimal size fraction for analysis, current techniques for geochemical analysis and ways to identify and mitigate potential contamination (both geogenic and anthropogenic). This paper also provides a comprehensive review of the major studies of regional humus surveys undertaken since the 1970s and summarizes case studies where humus has been used successfully to delineate precious (gold and platinum group elements), and critical metal (copper, lithium, nickel, and rare earth elements) mineralization in both undisturbed areas and those affected by anthropogenic contamination.
Near-infrared (NIR) spectroscopy is an emerging powerful tool with widespread applications across various industries, including mineral exploration and geometallurgical characterization. This technique is particularly attractive from an operational standpoint due to its ability to perform rapid and non-destructive measurements. In the geometallurgical field, NIR spectroscopy is primarily employed for the identification of NIR-active minerals that collectively influence the production process. A predictive mathematical model used to estimate a numeric value, like hardness, from an NIR spectral curve, may be referred to as a ‘chemometric model', represented by a function. The accuracy of chemometric models, like all predictive models, is highly reliant upon the availability of enough calibration samples to mathematically establish a relationship between the target variable and spectral signatures. The objective of this study is to present a methodology to evaluate the amount of calibration samples required to produce a sufficiently accurate chemometric model. An inverse function developed to simulate different scenarios of spectral curves is used. This tool enables the generation of multiple chemometric functions by varying the amount of data used for calibration. By employing an inverse function, it is possible to analyze how the number of calibration samples influences the associated variability or error. This approach allows for the establishment of a quantitative relationship between the calibration dataset size and the predictive model's reliability by calculating the percentage error from iterations using the simulated values. This study aims to determine the minimum number of samples required for the calibration of the chemometric model to ensure a prediction error below 3% for a specific target variable, demonstrated using hardness prediction. It is crucial that the selected calibration samples are spatially representative and free from sampling biases. It is important to highlight that the total error in the block model (for subsequent use in mine planning) results from the propagation of multiple sources of uncertainty, including quality assurance and quality control, chemometric model reliability, NIR sample representativity, and the interpolation methodology used in the block model. However, this analysis focuses exclusively on the variability associated with the reliability of the chemometric model. This methodology was tested in a simulated case study and in a porphyry copper deposit in northern Chile.
