Current Techniques and Applications of Mineral Chemistry to Mineral Exploration; Examples from Glaciated Terrain: A Review

This paper provides a summary of traditional, current, and developing exploration techniques using indicator minerals derived from glacial sediments, with a focus on Canadian case studies. The 0.25 to 2.0 mm fraction of heavy mineral concentrates (HMC) from surficial sediments is typically used for indicator mineral surveys, with the finer (0.25–0.50 mm) fraction used as the default grain size for heavy mineral concentrate studies due to the ease of concentration and separation and subsequent mineralogical identification. Similarly, commonly used indicator minerals (e.g., Kimberlite Indicator Minerals—KIMs) are well known because of ease of optical identification and their ability to survive glacial transport. Herein, we review the last 15 years of the rapidly growing application of Automated Mineralogy (e.g., MLA, QEMSCAN, TIMA, etc) to indicator mineral studies of several ore deposit types, including Ni-Cu-PGE, Volcanogenic Massive Sulfides, and a variety of porphyry systems and glacial sediments down ice of these deposits. These studies have expanded the indicator mineral species that can be applied to mineral exploration and decreased the size of the grains examined down to ~10 microns. Chemical and isotopic fertility indexes developed for bedrock can now be applied to indicator mineral grains in glacial sediments and these methods will influence the next generation of indicator mineral studies.

Geometallurgical Characterisation with Portable FTIR: Application to Sediment-Hosted Cu-Co Ores

Cobalt (Co) mine production primarily originates from the sediment-hosted copper (Cu) deposits of the Democratic Republic of Congo (DRC). These deposits usually consist of three ore zones with a supergene oxide ore blanket overlying a transition zone which grades into a sulphide zone at depth. Each of these zones display a mineral assemblage with varying gangue mineralogy and, most importantly, a distinct state of oxidation of the mineralisation. This has direct implications for Cu and Co extraction during mineral processing as it dictates which processing method is to be used (i.e., leaching vs. flotation) and affects the performance of these. To optimise resource efficiency, reduce technical risks and environmental impacts, comprehensive understanding of variation of ore mineralogy and texture in the deposit is essential. By defining geometallurgical ore types according to their inferred metallurgical behaviour, this information can serve to classify the resources and improve resource management. To obtain insight into the spatial distribution of mineral grades, it is necessary to develop techniques that have the potential to measure rapidly and, preferably, within the mine at relatively low-cost. In this study, the application of portable Fourier transformed infrared (FTIR) spectroscopy is investigated to measure the mineralogy of drill core samples. A set of samples from a sediment-hosted Cu-Co deposit in DRC was selected to test this approach. Results were validated using automated mineralogy (QEMSCAN). Prediction of gangue and target mineral grades from the FTIR spectra was achieved through partial least squares regression (PLS-R) combined with competitive adaptive reweighted sampling (CARS). It is shown that the modal mineralogy obtained from FTIR can be used to classify the ore according to type of mineralisation and gangue mineralogy into geometallurgical ore types. This classification supports selection of a suitable processing route and is likely to affect the overall process performance.

SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland

SEM-based automated mineralogy (SEM-AM) techniques allow fast and effective way of studying the textural settings of gold in hydrothermal deposits. Unsupervised machine learning (e.g. self-organizing maps) is an intuitive way of processing multi-dimensional geochemical datasets in order to reveal hidden patterns potentially represent different mineralization stages. We combined these two methods for studying the relationship of gold and cobalt within different prospects in a Paleoproterozoic gold-cobalt mineralized area known as Rajapalot. Gold is found as a texturally late phase, occurring in fractures of silicates and sulfides. Based on the elemental associations observed from the whole-rock geochemical dataset using self-organizing-maps, Co-only, Au-Co and Au associations can be inferred relating to either different mineralization stages or different fluid-host rock interactions. Also, the dominant mineralization-related alteration in different occurrences within the Rajapalot Au-Co prospects are reflected as elemental associations with gold in the geochemical data. Our study shows the effectiveness SEM-AM methods for studying distribution of valuable minerals. Unsupervised neural networks provide for easy and intuitive processing technique that can be validated with the mineralogical observations.

Multi-Scale X-Ray Computed Tomography Analysis to Aid Automated Mineralogy in Ore Geology Research

Ore characterization is crucial for efficient and profitable production of mineral products from an ore deposit. Analysis is typically performed at various scales (meter to microns) in a sequential fashion, where sample volume is reduced with increasing spatial resolution due to the increasing costs and run times of analysis. Thus, at higher resolution, sampling and data quality become increasingly important to represent the entire ore deposit. In particular, trace metal mineral characterization requires high-resolution analysis, due to the typical very fine grain sizes (sub-millimeter) of trace metal minerals. Automated Mineralogy (AM) is a key technique in the mining industry to quantify process-relevant mineral parameters in ore samples. Yet the limitation to two-dimensional analysis of flat sample surfaces constrains the sampling volume, introduces an undesired stereological error, and makes spatial interpretation of textures and structures difficult. X-ray computed tomography (XCT) allows three-dimensional imaging of rock samples based on the x-ray linear attenuation of the constituting minerals. Minerals are visually differentiated though not chemically classified. In this study, decimeter to millimeter large ore samples were analyzed at resolutions from 45 to 1 μm by AM and XCT to investigate the potential of multi-scale correlative analysis between the two techniques. Mineralization styles of Au, Bi-minerals, scheelite, and molybdenite were studied. Results show that AM can aid segmentation (mineralogical classification) of the XCT data, and vice versa, that XCT can guide (sub-)sampling (e.g., for heavy trace minerals) for AM analysis and provide three-dimensional context to the two-dimensional quantitative AM data. XCT is particularly strong for multi-scale analysis, increasingly higher resolution scans of progressively smaller volumes (e.g., by mini-coring), while preserving spatial reference between (sub-)samples. However, results also reveal challenges and limitations with the segmentation of the XCT data and the data integration of AM and XCT, particularly for quantitative analysis, due to their different functionalities. In this study, no stereological error could be quantified as no proper grain separation of the segmented XCT data was performed. Yet, some well-separated grains exhibit a potential stereological effect. Overall, the integration of AM with XCT improves the output of both techniques and thereby ore characterization in general.

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by X-ray computed micro-tomography (CT), here named mounted single particle characterization for 3D mineralogical analysis (MSPaCMAn). The method allows minimizing the impact of imaging artefacts that make the classification of voxels inaccurate and thus hinder the use of CT to characterize natural particulate materials. MSPaCMAn consists of (1) sample preparation as particle dispersions; (2) image processing optimized towards the labelling of individual particles in the sample; (3) phase identification performed at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an improved accuracy of phase classification, a higher number of detected phases, a smaller grain size that can be detected, and individual particle statistics can be measured instead of just bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited due to unreliable phase classification and quantification. MSPaCMAn could be the foundation of 3D semi-automated mineralogy similar to the commonly used 2D image-based semi-automated mineralogy methods.

3D Ore Characterization as a Paradigm Shift for Process Design and Simulation in Mineral Processing

Current advances and developments in automated mineralogy have made it a crucial key technology in the field of process mineralogy, allowing better understanding and connection between mineralogy and the beneficiation process. The latest developments in X‑ray micro-computed tomography (µCT) have shown a great potential to let it become the next-generation automated mineralogy technique. µCT’s main benefit lies in its capability to allow 3D monitoring of the internal structure of the ore sample at resolutions down to a few hundred nanometers, thus excluding the common stereological error in conventional 2D analysis. Driven by the technological and computational progress, µCT is constantly developing as an analysis tool and successively it will become an essential technique in the field of process mineralogy. This study aims to assess the potential application of µCT systems, for 3D ore characterization through relevant case studies. The opportunities and platforms that µCT 3D ore characterization provides for process design and simulation in mineral processing are presented.

Mineral Liberation: A Case Study for Buzwagi Gold Mine

Buzwagi Gold Mine (BGM) process plant was designed such that, after secondary grinding, gold and copper are recovered by flotation. However, the flotation circuit had been inefficient, and as a result, cyanidation of flotation tailings is currently conducted to improve gold recovery. The inefficient flotation is suspected to be due to mineralogical variations of ores treated. Hence, mineral liberation characteristics of three ore blends treated by BGM were investigated by automated Scanning Electron Microscope (SEM) whereby five fractions (i.e. -1 +0.5 mm, -0.5 +0.25 mm, -0.25 +0.125 mm, -0.125 +0.063 mm and -0.063 mm) were used. It was found that pyrite-pyrrhotite is the major valuable phase and the host of gold. Furthermore, pyrite-pyrrhotite was liberated at relatively coarse size (i.e. approx. 200-400 µm). Additionally, quartz, feldspar, muscovite and biotite-chlorite were the main gangue phases. Pyrite-pyrrhotite grain size distribution was coarser than most gangue minerals in the ore blends, indicating that most of the milling energy was lost in grinding of gangue phases. Since gold host phase (pyrite-pyrrhotite) was liberated at coarser sizes, it was concluded that the efficiency of gravity circuit could not be affected. However, the flotation process will still require finer feed (i.e. ≤ 125 µm) for its efficiency. Keywords: Mineral liberation; Gold ore blends; Flotation Performance; Pyrite-pyrrhotite; Automated Mineralogy