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.

The quest for steady primary supplies of critical raw materials (CRMs) creates significant waste, which is inevitably generated at each phase of mining and mineral processing. Waste from extraction, separation and refinement of non-renewable natural resources is accumulated globally and creates not only environmental hazards but also economic possibilities. Mine waste management is an expensive and prolonged task but unavoidable. Mine tailings, especially historical ones, can contain economically feasible resources, and given the right condition, such tailings could be reutilised through a waste valorisation concept. A prominent example are the Witwatersrand gold mine tailings in South Africa, which have been reused in small-scale projects. Tailing reutilisation is only possible if a sound classification, sampling and resource modelling framework is established to thoroughly and accurately profile the economic, environmental, health and geometallurgical aspects. Our study on valorisation of mine waste is presented in two parts: Here, in Part I, we focus on the essential components of a mine waste valorisation framework that includes the characterization and development of a systematic sampling framework for consolidated mineralised tailings. The development of a mine waste valorisation framework will hopefully enable worldwide reduction and reutilisation of mine waste.

Mine waste can create long-term and occasionally catastrophic environmental degradation. Due diligence of mine waste in the form of monitoring and maintenance requires a constant supply of societal resources. Furthermore, mine waste is unlikely to disappear with current mining methods and instead, it is more likely to accumulate at a faster rate due to decreasing primary ore grades and increasing societal demands. However, mine waste can be a societal asset, as it can offer an alternative source of partly critical raw materials (CRMs) that can augment primary sources and provide an opportunity to mitigate supply-risk while ensuring sustainability and easing geopolitical tensions. Cobalt is a critical raw material that is largely a by-product of mining of copper, nickel and platinum-group element ores. It is an element that the renewable energy and high-tech sectors critically depend on and for which no reasonable substitutes currently exist. The majority of the global cobalt production stems from the Central African Copperbelt. Published cobalt production figures for the Central African Copperbelt were used to evaluate cobalt tailings from the Central African Copperbelt. As part of a waste valorisation framework that focuses on primarily on the technical aspects of mine waste valorisation, this study assesses the application of key geostatistical methods, such as kriging and conditional simulation, followed by uniform conditioning, to evaluate the resource potential in a hypothetical copper-cobalt tailing deposit from the Central African Copperbelt. The results indicate that methods such as traditional algorithmic kriging, sequential Gaussian simulation and uniform conditioning are highly effective tools in resource modelling of mine waste. The resource assessment framework component developed in this study makes it possible to systematically characterise, profile and model any mine waste storage facility and thus supplements other framework components discussed in an accompanying paper to maximise mine waste utilization.