Applied Earth Science: Transactions of the Institutions of Mining and Metallurgy

Proterozoic limestone in a north British terrane contains a newly recognised occurrence of Nb-REE minerals. The mineralized Loch Shin Limestone is in the Lewisian Complex of the Northern Highlands of Scotland, intruded by alkaline plutons above a Caledonian (∼0.43 Ga) subduction zone. The mineral assemblage includes columbite, calcium niobate minerals, and niobian rutile and also includes W- and Sn-bearing phases. The interval between limestone deposition and mineralization was over a billion years. Other limestones in the Lewisian Complex that were not affected by alkaline plutons are not mineralized by Nb. The occurrence indicates that there may be exploration potential for Nb in limestones mineralized by hydrothermal activity above subduction zones with alkaline intrusions.

Carbonatite occurrences are reported in Lueshe, Kirumba, Bingo and Mombadio in the Democratic Republic of Congo (DRC), within the western branch of the East African Rift System (EARS). These rocks intrude Precambrian rocks, which are mainly quartzites and mica schists. Lateritic profiles from Lueshe and Bingo are ore-bearing minerals enriched in Nb₂O₅ and phosphate minerals. The Lueshe and Bingo exploitable quantities are estimated to be 30 Mt at a grade of 1.34% Nb₂O₅ and 7 Mt at a grade of 2.86% Nb₂O₅, respectively. These carbonatites were explored and exploited by some mining companies in the 1970s and 2000s. They show geological and geochemical similarities to other exploitable carbonatites in the EARS, including Mrima Hill in Kenya, Panda Hill in Tanzania and the world-class Araxá and Catalão carbonatites in Brazil. The Lueshe and Kirumba alkaline massifs dates, determined using the whole-rock Rb–Sr method, are respectively 822 ± 120 and 803 ± 22 Ma and are intimately linked to the Rodinia Supercontinent breakup. These dates are analogous to other regional carbonatite dates like the Matongo carbonatite in Burundi. However, further geological, petrological and geochemical studies on carbonatite complexes are essential in DRC. Most importantly, the economic potentials of Kirumba and Mombadio should be evaluated. Simultaneously, the dates of the Bingo and Mombadio carbonatites are crucial for the elucidation of their geodynamic settings.

The beach sands of the Varkala-Kovalam coast, south-west India, are enriched with heavy minerals with a high concentration of ilmenite followed by sillimanite, monazite, rutile, zircon, and garnet. The Fe–Ti oxide minerals such as ilmenite, its altered product leucoxene, and rutile were successfully recovered, and their structure, chemistry, and surface morphology were analysed using advanced characterisation techniques like Raman spectroscopy, Energy Dispersive X-ray Fluorescence (ED-XRF), High Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICP-MS), Thermogravimetric Analysis (TGA), UV-Visible-NIR (UV-Vis-NIR) spectroscopy, X-ray Photoelectron Spectroscopy (XPS), and Scanning Electron Microscopy (SEM-EDS). The chemical composition, surface chemistry, the oxidation state of surface elements, anisotropic crystal behaviour of the minerals due to physical or chemical processes, discrimination of polymorphs, and finally the morphological changes due to mechanical impacts were also analysed. The study provides solid information to the scientific community and policymakers for determining the grade and potential applications of these strategic minerals.

Grade models built by traditional estimation or simulation methods often fail to reproduce the complex relationships between the variables. This work investigates the use of the multivariate transformation called Projection Pursuit Multivariate Transform (PPMT), which fully decorrelates the multiple variables of interest, allowing the independent conditional simulation of each variable in the transformed space. Finally, the simulated variables are back-transformed, reproducing the initial correlations of the data. The PPMT workflow was applied to a nickel laterite deposit considering five variables: nickel, iron, silica, magnesium, and calcium grades. Conditional simulations of each variable were run and validated. The back-transformed realisations reproduced the multivariate relationships of the data. To calculate the uncertainties, mining panels equivalent to two and four weeks of production were generated using the k-means clustering technique. Uncertainties were summarised by the coefficient of variation (CV) and the results were used to define classes of mineral resources.

Kriging methods require parameters to define search strategy (kriging neighbourhood). These parameters affect the precision and accuracy of its estimates. Frequently, the choice of these parameters is merely subjective. Some practitioners prioritise estimates that lead to models with a reduced smoothing effect or a regression slope as close as possible to one. However, it is prevalent to use the same kriging neighbourhood or search strategy for all blocks estimated within a stationary domain. This study presents a contribution that challenges this concept by using a block-by-block optimisation approach focused on the localised kriging parameter optimisation (LKPO) methodology. A comparative study is carried out, and some of the metrics analysed include the kriging efficiency and the slope of regression (typical in optimising methodologies in the mining industry). The results indicate that the LKPO methodology provides more accurate and precise estimates than those based on a global kriging neighbourhood.