Mineral exploration in the Arctic is challenging, resulting in decreased survey and mineral discovery rates. Data-driven mineral prospectivity mapping is intended to reduce exploration risk by delineating promising areas. However, data sparsity, especially of deposit locations, is a critical bottleneck. In this study, we developed and tested a method to conduct data-driven mineral prospectivity mapping in regions where annotated data are too sparse. Our method is predicated on the existence of large-scale mineral prospectivity frameworks (data, models and methods), which are fine-tuned using regional ground truth to produce locally refined predictions. This is possible because the spatial transferability of trained machine learning models is incomplete. We explicitly tested the hypothesis of limited spatial transferability. To within limitations of our experimental design, our results are supportive of our hypothesis that the incorporation of new positive samples into an existing training dataset preferentially influences the local outcome. Using a suite of critical minerals combined with published data and models in Canada, we demonstrated that by treating large-scale mineral prospectivity frameworks as foundational, it is productive to fine-tune trained models using regional data. Although national-scale maps exist prior to this study, they did not incorporate a substantial amount of ground truth from Nunavut. Therefore, through regional refinement, we produced the inaugural version of a set of pan-Canadian mineral prospectivity maps that are specifically refined for Nunavut for: (1) flake graphite; (2) magmatic Ni (±Cu ±Co ±PGE); and (3) Mississippi Valley-type Pb–Zn. The regional refinement method is a perspectivist and federated approach to MPM that extracts additional value from large-scale mineral prospectivity frameworks and provides an interoperable method for national, regional and inter-regional mineral exploration.

Exploration for graphite in Canada is of economic, strategic and governance priority. In this study, we aimed to develop a reliable prospectivity map for graphite in Canada. Our approach mitigated multiple sources of workflow-induced uncertainty by propagating uncertainty due to the selection of negative labels, machine learning algorithms, feature space dimensionality, and hyperparameter tuning metrics. By averaging an ensemble of de-correlated models, we produced a single-merged model that clearly represents propagated uncertainty through a consensus map and an uncertainty map. These maps adhere to the metrological convention of "result plus/minus associated uncertainty" and are intuitive to use. Our ensemble demonstrated robustness, quickly converging to the consensus model, suggesting that new mineral prospectivity mapping (MPM) products using the same data would unlikely perturb our consensus model’s coverage. We conducted a maximally double-blind study, avoiding geoscientific knowledge during model generation to ensure impartial post-hoc analysis and interpretation. Therefore, our MPM products complement geoscientific knowledge-based exploration, because the targeting information provided in our MPM products constitute a maximally independent source. Our MPM products showed excellent spatial variability, aligning with existing knowledge of graphite deposits in Canada, indicating that combining data-driven rigor with independent interpretation enhances the robustness of our MPM products. Consequently, we believe our MPM products could effectively guide regional exploration of natural graphite in Canada.

Understanding near‐surface groundwater storage, flow patterns, surface and groundwater interactions in mining areas can assist in making mining more efficient and profitable. This is especially important in opencast mines affected by water inflows that may negatively affect production and increase mining costs. We map and characterize the near‐surface aquifer zones at the opencast site of Tharisa Minerals, located in the southwestern region of the Bushveld Complex (South Africa). The main goal is to infer pit water inflow at the mine site and determine how it may be better controlled. The Bushveld Complex hosts partially connected and unconfined alluvial, shallow‐weathered and crystalline bedrock aquifers, which are often connected by small‐scale permeable zones. Seismic refraction tomography, multichannel analysis of surface waves, electrical resistivity tomography and borehole data are used to map and understand the different aquifer zones in the vicinity of the mine, as well as infer their relation to water inflow in the mine pits. The geophysical surveys map the overburden, weathered bedrock aquifer zone, and the top of the crystalline aquifer rock zone reasonably well. They reveal extensive and deep weathering, and possible high hydraulic conductivity in the vicinity of the mine. The results provide a better understanding of the mine's near‐surface environment, which could be used to implement effective and targeted dewatering techniques, thus enabling better pit inflow water control to improve mine working conditions and production.

We present here the first experimental science (consensus)-based mineral prospectivity mapping (MPM) method and its validation results in the form of national prospectivity maps and datasets for PGE–Ni–Cu–Cr and Witwatersrand-type Au deposits in South Africa. The research objectives were: (1) to develop the method toward applicative uses; (2) to the extent possible, validate the effectiveness of the method; and (3) to provide national MPM products. The MPM method was validated by targeting mega-deposits within the world’s largest and best exploited geological systems and mining districts—the Bushveld Complex and the Witwatersrand Basin. Their incomparable knowledge and mega-deposit status make them the most useful for validating MPM methods, serving as “certified reference targets”. Our MPM method is built using scientific consensus via deep ensemble construction, using workflow experimentation that propagates uncertainty of subjective workflow choices by mimicking the outcome of an ensemble of data scientists. The consensus models are a data-driven equivalent to expert aggregation, increasing confidence in our MPM products. By capturing workflow-induced uncertainty, the study produced MPM products that not only highlight potential exploration targets but also offer a spatial consensus level for each, de-risking downstream exploration. Our MPM results agree qualitatively with exploration and geological knowledge. In particular, our method identified areas of high prospectivity in known exploration regions and geologically and geospatially corresponding to the known extents of both mineral systems. The convergence rate of the ensemble demonstrated a high level of statistical durability of our MPM products, suggesting that they can guide exploration at a national scale until significant new data emerge. Potential new exploration targets for PGE–Ni–Cu–Cr are located northwest of the Bushveld Complex; for Au, promising areas are west of the Witwatersrand Basin. The broader implications of this work for the mineral industry are profound. As exploration becomes more data-driven, the question of trust in MPM products must be addressed; it can be done using the proposed scientific method.

This report presents the geochemical data, quality assurance and quality control (QA/QC) results of the re-analysis of lake sediment samples collected from north-western Manitoba (NTS 064-F). The original survey was conducted in 1984 and the re-analysis in 2021. Original survey results are presented in OF 1104. A total of 1,024 lake sediment samples were re-analyzed, covering an area of 13,400 km2, averaging a density of 1 sample per 13 km2. Samples were analyzed for 65 elements via modified aqua-regia - ICP-MS and 35 elements via INA. To ensure high quality data, the geochemical data was evaluated for contamination, accuracy, precision and fitness-for-purpose (ANOVA). QA/QC results have identified a number of elements to be monitored carefully for future analyses. Overall, the dataset is of good quality.

Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.

The primary goal of mineral prospectivity mapping (MPM) is to narrow the search for mineral resources by producing spatially selective maps. However, in the data-driven domain, MPM products vary depending on the workflow implemented. Although the data science framework is popular to guide the implementation of data-driven MPM tasks, and is intended to create objective and replicable workflows, this does not necessarily mean that maps derived from data science workflows are optimal in a spatial sense. In this study, we explore interactions between key components of a geodata science-based MPM workflow on the geospatial outcome, within the modeling stage by modulating: (1) feature space dimensionality, (2) the choice of machine learning algorithms, and (3) performance metrics that guide hyperparameter tuning. We specifically relate these variations in the data science workflow to the spatial selectivity of resulting maps using uncertainty propagation. Results demonstrate that typical geodata science-based MPM workflows contain substantial local minima, as it is highly probable for an arbitrary combination of workflow choices to produce highly discriminating models. In addition, variable domain metrics, which are key to guide the iterative implementation of the data science framework, exhibit inconsistent relationships with spatial selectivity. We refer to this class of uncertainty as workflow-induced uncertainty. Consequently, we propose that the canonical concept of scientific consensus from the greater experimental science framework should be adhered to, in order to quantify and mitigate against workflow-induced uncertainty as part of data-driven experimentation. Scientific consensus stipulates that the degree of consensus of experimental outcomes is the determinant in the reliability of findings. Indeed, we demonstrate that consensus through purposeful modulations of components of a data-driven MPM workflow is an effective method to understand and quantify workflow-induced uncertainty on MPM products. In other words, enlarging the search space for workflow design and experimenting with workflow components can result in more meaningful reductions in the physical search space for mineral resources.

Mineral resources are important contributors to the global economy and societal wellbeing. Directly, they provide employment, revenue and taxes through the extraction, processing and sale of minerals. Indirectly, they are essential to all modern industries, including: energy, manufacturing, construction, biotic and abiotic resource extraction and agriculture. The principle that 'one cannot understand the value of what they have until they measure it ' is particularly relevant with critical raw materials (CRMs). CRM is a concept that categorises select resources (mainly minerals and metals) as critical in the sense that, at a national level, they are essential and difficult to replace, and their supply is prone to disruption. It is becoming increasingly recognised that the continuity of civilisation and living standards as some have envisioned them in the future is constrained by the quality and quantity of various minerals. National-level strategic planning, including energy policy, foreign relations policy, geopolitical operations, national defence, education and infrastructure planning, among others, all require knowledge of the requirement and supply of raw materials towards a practical strategic implementation. Hence, a national CRM framework is essential for a prosperous, productive and stable future. To effectively manage the supply and use of CRMs, it is important to comprehend both their formal (e.g., economic) and informal (e.g., social and environmental) values, and to measure and monitor these values effectively over time. This study examines international practices and methodologies as components of a comprehensive CRM framework. We then propose a prototype CRM framework for South Africa as such a framework is currently missing. All CRM frameworks feature one or more rating schemes to identify the degree of criticality of raw materials. The actual rating metrics are divided into dimensions (or factors), such as: socio-economic importance, technological importance, environmental, social and governance risks. Such dimensions are important due to the following reasons.

The energy sector is currently undergoing a transition towards increased utilization of green energy technologies. The green energy transition relies heavily on metals, such as aluminium, chromium, cobalt, copper, lithium, manganese, nickel, rare earth elements (REEs), silicon, tin, titanium, tungsten and zinc, among others. However, this transition occurs within the context of: (1) a geographical concentration of known mineral deposits and downstream capability; (2) a demand that vastly exceeds supply; (3) a strong drive to mitigate environmental and energy concerns; and (4) an increasing level of geopolitical conflicts. Consequently, the energy transition is not straightforward, as it intensifies material demand, market and geopolitical competition. This is especially true for lithium which is pivotal in this transformation. Concerns driven by material access, energy sustainability and national sufficiency are increasingly resulting in national and super-national geopolitical activities, such as resource nationalisation, forming strategic or trade alliances, encouraging near- and friend-shoring, promoting material circularity, and accelerating green technology research and deployment. This study examines the global impact of the green energy transition, from the perspective of the mineral value chain, including downstream products, its implications on the projected demand, and geopolitics. There are many potential outcomes in the future, depending on: (1) the pragmatic outcomes of the energy transition, which is only empirically realizable through implementation; (2) the strength of independence and global stability; and (3) the rise of regional or friendly trade blocs. In particular, this study examines a future scenario in which there is an emergence of an OPEC-style organisation for green energy minerals and metals (GEMMs), focusing on lithium as an example, because: (1) it has a clear and essential role in the green energy transition; (2) it is geographically concentrated in a manner that facilitates production coordination; and (3) it is overwhelmingly consumed by developed nations but supplied by developing nations. An organisation built around lithium could be a prototype to other GEMM markets. Consequently, we propose that it is possible to leverage existing circumstances and propose that such an organisation, here termed Green Energy-Mineral Exporting Countries, or "GEMEC", could serve as a collaborative platform to enhance geopolitical positioning, maximise economic benefits through coordinated production and export policies, and address the environmental, social and governance challenges associated with the green energy transition.

Geochemical surveys are a cornerstone for data generation in geosciences, facilitating resource exploration. Geochemical data is essential for identifying mineralized areas, understanding local geology and assessing environmental impacts. This paper aims to: (1) review the process of geochemical data generation to gain an appreciation of the characteristics of traditional geochemical data; (2) review recent developments in the usage of geochemical data, particularly the disruption brought forth by those in data science and artificial intelligence; and (3) envision a future specification of geochemical data that would be fit-for-purpose for modern and emerging data users. This review reveals that benefits brought by advancements in automation, analytical technique and computing capability have unlocked unprecedented insights from geochemical data. However, the sustainability of re-purposing small geochemical data for big data methods is intrinsically questionable. The mismatch stems from rapidly changing data requirements in geochemistry, which is brought forth by: (1) a pivot away from scientific reduction through the adoption of system-level methods; (2) developments in geometallurgy; (3) skill gaps in geoscientific education; and (4) a growing demand of raw materials. While traditional methods will likely continue to serve many scientific needs, new strategies and techniques must be developed and implemented to cost effectively and efficiently generate bigger geochemical data. Bigger geochemical data must emerge in response to the already changing landscape of geochemical data usage. Solutions are multidimensional, from evolving geoscientific education, leveraging modern technology, explicating and differentiating data user and generator roles and responsibilities, to modernizing data management.

Carbonatites are the primary geological sources for rare earth elements (REEs) and niobium (Nb). This study applies machine learning techniques to generate national-scale prospectivity models and support mineral exploration targeting of Canadian carbonatite-hosted REE +/− Nb deposits. Extreme target feature label imbalance, diverse geological settings hosting these deposits throughout Canada, selecting negative labels, and issues regarding the interpretability of some machine learning models are major challenges impeding data-driven prospectivity modeling of carbonatite-hosted REE +/− Nb deposits. A multi-stage framework, exploiting global hierarchical tessellation model systems, data-space similarity measures, ensemble modeling, and Shapley additive explanations was coupled with convolutional neural networks (CNN) and random forest to meet the objectives of this work. A risk–return analysis was further implemented to assist with model interpretation and visualization. Multiple models were compared in terms of their predictive ability and their capability of reducing the search space for mineral exploration. The best-performing model, derived using a CNN that incorporates public geoscience datasets, exhibits an area under the curve for receiver operating characteristics plot of 0.96 for the testing labels, reducing the search area by 80

This report presents the geochemical data, quality assurance and quality control (QA/QC) results of the re-analysis of lake sediment samples collected from north-central Saskatchewan (NTS 074-A, B, G and H). The original lake survey was conducted in 1986 and the re-analysis in 2021. Original survey results are presented in OF1359. A total of 1,290 lake sediment samples were re-analyzed, covering an area of 17,000 km2, averaging a density of 1 sample per 13 km2. Samples were measured for 65 elements via modified aqua-regia - ICP-MS and 35 elements via INA analysis. To ensure high quality data, the geochemical data was evaluated for contamination, accuracy, precision and fitness-for-purpose. QA/QC results have identified a number of elements to be monitored carefully for future analyses. Overall, the data are of good quality.

Regional-scale lake sediment surveys have been successfully used since the 1970s as a means for reconnaissance geochemical exploration. Lake sediment sampling is typically performed in areas with a lack of streams and an overabundance of small-sized (=5 km across) lakes. Lake sediments are known to have major, minor and trace element concentrations that reflect the local geology. Overall, lake sediment surveys are planned and conducted following four distinct stages: 1) background research, 2) orientation survey, 3) regional survey, and 4) detailed survey. At the Geological Survey of Canada, samples are usually collected from a helicopter with floats. Sample density ranges from 1 sample per 6 - 13 km2. Samples are collected from the centre of the lake using a gravity torpedo sampler which corresponds to a hollow-pipe, butterfly bottom-valved sampler attached by a rope to the helicopter. Collected sediment samples are then placed in labelled bags and left to air dry. Detailed field notes and additional samples (field duplicates), for the purpose of an adequate quality assurance and quality control program, are also taken. Samples are then milled and sent to analytical laboratories for element determination. Commonly used analytical methods include: X-ray fluorescence (XRF), atomic absorption spectroscopy (AAS), inductively coupled plasma-atomic emission spectrometry (ICP-AES) and -mass spectrometry (ICP-MS), instrumental neutron activation analysis (INAA), and/or determination of volatile compounds and organic carbon using Loss on Ignition (LOI). Analytical data is first evaluated for quality (contamination, accuracy and precision). Numerous options for the analysis of lake sediment data exist, ranging from simple basic element concentration maps and statistical graphical displays together with summary statistics, to employing multivariate methodologies, and, more recently, using machine learning algorithms. By adopting the set of guidelines and examples presented in this manual, scientific researchers, exploration geologists, geochemists and citizen scientists will be able to directly compare lake sediment datasets from anywhere in Canada.

The green energy transition is aimed at mitigating the impact of climate change. Yet, the current emphasis on ‘green’ is narrowly centred around decarbonisation, or CO2 reduction, often side-lining the roles of other gases, such as sulphur hexafluoride (SF6) and PCF-14 (CF4), which have a respective 24,300- and 7380-times higher global warming potential than CO2 on a time horizon of a century. In addition, any energy transition is a complex affair that simultaneously impacts the environmental, economic and social systems, with significant system-level interactions. For example, the material requirement for renewable energy is known to be substantial, at a factor of 15 times greater than natural gas-based energy for offshore wind generation, and almost 7 times greater for solar. The resulting increased competition for materials is reducing the appetite for global collaboration. In addition, the global capacity to deploy renewable energy technology or participate in climate change mitigation is geographically variable and no single solution is universally viable. This study examines an expanded definition of ‘green’ energy and proposes a beyond-decarbonisation approach that is more comprehensive and globally inclusive, in pursuit for a sustainable transition. The increased diversity of our approach promises advantages such as heightened global collaboration, diminished geopolitical tension, improved energy access, expanded market opportunities, and socio-environmental co-benefits. Strategies pivotal to this approach involve understanding the role of carbon-based energy systems in the transition, amplifying renewable resources, augmenting cross-sector energy efficiency, implementing effective carbon markets, and integrating nature-based as well as carbon removal technologies. Moreover, it is imperative to implement all-cost and all-benefit monitoring and evaluation systems to optimise existing decarbonisation methods systematically. This could entail the use of composite metrics that normalise the gain in climate change mitigation against economic, social or environmental metrics. Addressing societal apprehensions requires a focus on pragmatic and fair outcomes, geopolitical stability, market impacts, developmental objectives, effective public engagement, and recognition of the role of enterprises. Policymakers are important in fostering global synergy by implementing policies that encourage international collaboration, investment, enterprise engagement, institution fortification, and cross-sector policy integration.

In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a central technique since the advent of geostatistics. Here, we introduce a new method for spatial interpolation in 2D and 3D using a block discretization technique (i.e., microblocking) using purely machine-learning algorithms and workflow design. This paper addresses the challenges of modeling spatial patterns and regularities in nature, and how different approaches have been used to cope with these challenges. We specifically explore the advantages and drawbacks of kriging while highlighting the long and complex sequence of procedures associated with block kriging. We argue that machine-learning techniques offer opportunities to simplify and streamline the process of mapping and mineral resource estimation, especially in cases of strong spatial relationships between sample location and resource concentration. To test the new method, synthetic 2D and 3D data were used for both 2D block modeling and geometallurgical modeling of a synthetic porphyry Cu deposit. The synthetic porphyry Cu data were very useful in validating the performance of the proposed microblocking technique as we were able to reproduce known values at unsampled locations. Our proposed method delivers the benefits of a machine learning-based block modeling approach, which includes its simplicity (a minimum of 2 hyperparameters), speed and familiarity to data scientists. This enables data scientists working on spatial data to employ workflows familiar to their training, to tackle problems that were previously solely in the domain of geoscience. In exchange, we expect that our method will be a gateway to attract more data scientist to become geodata scientists, benefitting the modern data-driven mineral value chain.

Real-time information management systems (RTIMS) are emerging for the mining industry. RTIMS are created to enable minimal-latency (real-time) of information delivery and usage to enable dynamic and smart decision making. However, being an emerging concept, it is unclear what the target readiness of RTIMS should be. As a result, system owners may face challenges identifying appropriate RTIMS performance models. In this study, RTIMS is treated as an integration of information, as a resource and as the systems for managing this resource, in real-time, across all phases of the mining value chain. Consequently, the capability maturity assessment tool for RTIMS must consider: information as the critical resource; the management systems for the critical resource; and the access to the resource in real-time. Thus, a hybridised maturity model is proposed for the RTIMS maturity target, which can be further optimised through stakeholder engagement. In our proposed framework, three elements namely, people, the environment, and technology, have been identified as key enablers for RTIMS in the minerals sector. In addition, we propose a six-level capability maturity model, with levels ranging from 0 to 5, signifying the state of no recognition of the processes to an optimised state, where prevention and maintenance are the norm.

Critical Raw Materials (or CRMs) are materials that are in high demand, difficult to replace and whose supply is prone to disruption. Various nations have defined CRM lists, although terminology, supporting data and assessment frameworks differ. The European Union (EU) has the longest published history of CRM lists with the first one published in 2011, followed by 3-year revisions. In this study, we analyze CRM designation trends over time by using the EU's five CRM lists to deduce the driving factors. Overall, the number of CRMs have increased by 1.67 new CRMs per year from 2011 to 2023, with the number of new CRMs yet to reach a plateau. Our analysis also reveals issues that could affect the value of the CRM lists including: (1) a hidden two-stage process with transparency issues; (2) static baselines with regards to criticality; (3) an overemphasis on ideology versus pragmatism; (4) a lack of differentiation between CRMs and strategic raw materials (SRMs); (5) a lack of foresight; and (6) a lack of consideration for extrinsic risks and system behaviour. Given these issues, we provide suggestions to improve the CRM assessment methodology and discuss the implications for the EU and the minerals industry. Subsequently, we extend our findings to Canada and South Africa, which are nations in the early stages of CRM framework creation. We find that Canada has more time to realize its CRM framework as compared to the EU, and that South Africa may be faced with a bifurcating reality of extra-national and national needs. Our findings also highlight serious geopolitical implications with the ensuing competition for resources likely resulting in the formation of economic blocs, clubs or cartels. Finally, improvements to the methodology resulting in more predictable outcomes would better incentivize the minerals industry to lower investment risk and ensure a smooth and pragmatic green energy transition.

Many features indicative of natural gas and oil leakage are delineated in the deep-water Orange Basin offshore South Africa using 3D reflection seismic data. These features are influenced by the translational and compressional domains of an underlying Upper Cretaceous deep-water fold-and-thrust belt (DWFTB) system detaching Turonian shales. The origin of hydrocarbons is postulated to be from both: (a) thermogenic sources stemming from the speculative Turonian and proven Aptian source rocks at depth; and (b) biogenic sources from organic-rich sediments in the Cenozoic attributed to the Benguela Current upwelling system. The late Campanian surface has a dense population of > 950 pockmarks classified into three groups based on their variable shapes and diameter: giant (> 1500 m), crater (~ 700–900 m) and simple (< 500 m) pockmarks. A total of 85 simple pockmarks are observed on the present-day seafloor in the same area as those imaged on the late Campanian surface found together with mass wasting. A major slump scar in the north surrounds a ~ 4200 m long, tectonically controlled mud volcano. The vent of the elongated mud volcano is near-vertical and situated along the axis of a large anticline marking the intersection of the translational and compressional domains. Along the same fold further south, the greatest accumulation of hydrocarbons is indicated by a positive high amplitude anomaly (PHAA) within a late Campanian anticline. Vast economical hydrocarbon reservoirs have yet to be exploited from the deep-water Orange Basin, as evidenced by the widespread occurrence of natural gas/fluid escape features imaged in this study.