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  • Demand for critical raw materials is expected to accelerate over the next few decades due to continued population growth and the shifting consumption patterns of the global economy. Sedimentary basins are important sources for critical raw materials and new discoveries of sediment–hosted Mississippi Valley–type (MVT) and/or clastic–dominated (CD) Zn–Pb deposits are likely required to mitigate future supply chain disruptions for Zn, Pb, Ag, Cd, Ga, Ge, Sb, and In. Herein we integrate public geoscience datasets using a discrete global grid to system to model the mineral potential for MVT and CD deposits across Canada, the United States of America, and Australia. Statistical analysis of the model results demonstrates that surface–wave tomography and derivative products from satellite gravity datasets can be used to map the most favourable paleo–tectonic settings of MVT and CD deposits inboard of orogenic belts and at the rifted edges of cratonic lithosphere, respectively. Basin development at pre–existing crustal boundaries was likely important for maintaining the low geothermal–gradients that are favourable for metal transport and generating the crustal fluid pathways that were reactivated during ore–formation, as suggested by the statistical association of both sediment–hosted mineral deposit types with the edges of upward–continued gravity and long–wavelength magnetic anomalies. Multivariate statistical analysis demonstrates that the most prospective combination of these geophysical datasets varies for each geological region and deposit type. We further demonstrate that maximum and minimum geological ages, coupled with Phanerozoic paleogeographic reconstructions, represent mappable proxies for the availability of oxidized, brine–generating regions that are the most likely source of ore–forming fluids (e.g., low– to mid–latitude carbonate platforms and evaporites). Ore deposition was likely controlled by interaction between oxidized, low–temperature brines and sulfidic and/or carbonaceous rocks, which, in some cases, can be mapped at the exposed surface or identified using the available rock descriptions. Baseline weights–of–evidence models are based on regional geophysics and are the least impacted by missing surface information but yield relatively poor results, as demonstrated by the low area–under–the–curve (AUC) for the spatially independent test set on the success–rate plot (AUC = 0.787 for MVT and AUC = 0.870 for CD). Model performance can be improved by: (1) using advanced methods that were trained and validated during a series of semi–automated machine learning competitions; and/or (2) incorporating geological and geophysical datasets that are proxies for each component of the mineral system. The best–performing gradient boosting machine models yield higher AUC for the test set (AUC = 0.983 for MVT and AUC = 0.991 for CD) and reduce the search space by >94%. The model results highlight the potential benefits of mapping sediment–hosted mineral systems at continental scale to improve mineral exploration targeting for critical raw materials.

  • Heavy minerals (HMs) are minerals with a specific gravity greater than 2.9 g/cm3. They are commonly highly resistant to physical and chemical weathering, and therefore persist in sediments as lasting indicators of the (former) presence of the rocks they formed in. The presence/absence of certain HMs, their associations with other HMs, their concentration levels, and the geochemical patterns they form in maps or 3D models can be indicative of geological processes that contributed to their formation. Furthermore trace element and isotopic analyses of HMs have been used to vector to mineralisation or constrain timing of geological processes. The positive role of HMs in mineral exploration is well established in other countries, but comparatively little understood in Australia. Here we present the results of a pilot project that was designed to establish, test and assess a workflow to produce a HM map (or atlas of maps) and dataset for Australia. This would represent a critical step in the ability to detect anomalous HM patterns as it would establish the background HM characteristics (i.e., unrelated to mineralisation). Further the extremely rich dataset produced would be a valuable input into any future machine learning/big data-based prospectivity analysis. The pilot project consisted in selecting ten sites from the National Geochemical Survey of Australia (NGSA) and separating and analysing the HM contents from the 75-430 µm grain-size fraction of the top (0-10 cm depth) sediment samples. A workflow was established and tested based on the density separation of the HM-rich phase by combining a shake table and the use of dense liquids. The automated mineralogy quantification was performed on a TESCAN® Integrated Mineral Analyser (TIMA) that identified and mapped thousands of grains in a matter of minutes for each sample. The results indicated that: (1) the NGSA samples are appropriate for HM analysis; (2) over 40 HMs were effectively identified and quantified using TIMA automated quantitative mineralogy; (3) the resultant HMs’ mineralogy is consistent with the samples’ bulk geochemistry and regional geological setting; and (4) the HM makeup of the NGSA samples varied across the country, as shown by the mineral mounts and preliminary maps. Based on these observations, HM mapping of the continent using NGSA samples will likely result in coherent and interpretable geological patterns relating to bedrock lithology, metamorphic grade, degree of alteration and mineralisation. It could assist in geological investigations especially where outcrop is minimal, challenging to correctly attribute due to extensive weathering, or simply difficult to access. It is believed that a continental-scale HM atlas for Australia could assist in derisking mineral exploration and lead to investment, e.g., via tenement uptake, exploration, discovery and ultimately exploitation. As some HMs are hosts for technology critical elements such as rare earth elements, their systematic and internally consistent quantification and mapping could lead to resource discovery essential for a more sustainable, lower-carbon economy.

  • This web map service provides visualisations of datasets prepared for the Technology Investment Roadmap Data Portal. The service has been developed using various industrial plant location datasets sourced from the Australia’s Identified Mineral Resources (2019), produced by Geoscience Australia (http://dx.doi.org/10.11636/1327-1466.2019)

  • This web map service provides visualisations of datasets prepared for the Technology Investment Roadmap Data Portal. The service has been developed using various industrial plant location datasets sourced from the Australia’s Identified Mineral Resources (2019), produced by Geoscience Australia (http://dx.doi.org/10.11636/1327-1466.2019)

  • This dataset provides the locations and status, as at 30 June 2020, of Australian operating mines, mines under development, mines on care and maintenance and resource deposits associated with critical minerals. Developing mines are deposits where the project has a positive feasibility study, development has commenced or all approvals have been received. Mines under care and maintenance and resource deposits are based on known resource estimations and may produce critical minerals in the future.

  • Bluecap is an open-source python software library developed through a collaboration between Monash University and Geoscience Australia. The software enables geospatial economic simulation of Australian resource projects. The simulator's goal is to highlight regions of high potential value in the early planning/exploration phase. Bluecap is designed to assist companies in focusing their efforts on regions more likely to generate commercially-viable projects. It was initially developed for the purpose of supporting mineral exploration, and has recently been expanded to include the capability to model hydrogen production. The simulator is a pre-scoping tool that uses coarse-level empirical models to compare project prospects across large areas. Due to its broad scale, Bluecap lacks the detailed information necessary for full feasibility studies, and as such, it should not be used as the sole basis for investment decisions. The Bluecap software underpins Geoscience Australia's Hydrogen Economic Fairways Tool (HEFT) and Economic Fairways Mapper. If you use Bluecap for a publication, please cite the following: Walsh, S.D.C., Northey, S.A., Huston, D., Yellishetty, M. and Czarnota, K. (2020) Bluecap: A Geospatial Model to Assess Regional Economic-Viability for Mineral Resource Development, Resources Policy. Geoscience Australia eCat number: 132645