The Mineral Potential web service provides access to digital datasets used in the assessment of mineral potential in Australia. The service includes maps showing the potential for sediment-hosted base metal mineral systems in Australia.

<div>The Proterozoic alkaline and related igneous rocks of Australia is a surface geology compilation of alkaline and related igneous rocks of Proterozoic age in Australia. This dataset is one of five datasets, with compilations for Archean, Paleozoic, Mesozoic and Cenozoic alkaline and related igneous rocks already released.</div><div><br></div><div>Geological units are represented as polygon and point geometries and, are attributed with information that includes, but is not limited to, stratigraphic nomenclature and hierarchy, age, lithology, composition, proportion of alkaline rocks, body morphology, unit expression, emplacement type, presence of mantle xenoliths and diamonds, and primary data source. Source data for the geological unit polygons provided in Data Quality LINEAGE. Geological units are grouped into informal geographic “alkaline provinces”, which are represented as polygon geometries, and attributed with information similar to that provided for the geological units.</div>

Magnetotelluric (MT) data allow geoscientists to investigate the link between mineralisation and lithospheric-scale features and processes. In particular, the highly conductive structures imaged by MT data appear to map the pathways of large-scale palaeo-fluid migration, which is an important element of several mineral systems. New data were collected as part of the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) under Geoscience Australia Exploring for the Future (EFTF) program in northern Australian. We use this dataset to demonstrate that the MT method is a valuable tool for mapping lithospheric-scale features and for selecting prospective areas for mineral exploration. Our results image a number of major conductive structures at depths up to ~200 km or deeper in the survey region, for example, the Carpentaria Conductivity Anomaly in east of Mount Isa; and the Tanami Conductive Anomaly along the Willowra Suture Zone. These significant anomalies are lithospheric- scale highly conductive structures, and show spatial correlations with major suture zones and known mineral deposits. These results provide important first-order information for lithospheric architecture and possible large footprint of mineral systems. Large-scale crustal/mantle conductivity anomalies mapping fluid pathways associated with major sutures/faults may have implications for mineral potential. These results provide evidence that some mineralisation occurs at the gradient of or over highly conductive structures at lower crustal and lithospheric mantle depths. These observations provide a powerful means of highlighting greenfields for mineral exploration in under-explored and covered regions.

<div>Australian sediment-hosted mineral systems are important sources of base metals and critical minerals that are vital to delivering Australia’s low-carbon economy. In Australia, sediment-hosted resources account for ~82% and ~86% of the total zinc (Zn) and lead (Pb) resources respectively. Given their significance to the Australian economy, four national-scale mineral potential models for sediment-hosted Zn-Pb mineral systems have been developed: clastic-dominated siliciclastic carbonate, clastic-dominated siliciclastic mafic, Mississippi Valley-type and Irish-type. In addition to the potential for Zn-Pb mineralisation, the uncertainty related to data availability has been examined. The mineral potential models were created using a mineral systems-based approach where mappable criteria have been used to assess the prospectivity of each system. Each model has been derived from a large volume of precompetitive geoscience data. The clastic-dominated siliciclastic carbonate mineral potential model predicts 92% of known deposits and occurrences within 15.5% of the area, the clastic-dominated siliciclastic mafic mineral potential model predicts 85% of deposits and occurrences within 27% of the area, and the Mississippi Valley-type mineral potential model predicts 66% of known deposits and occurrences within 31% of the area. Each model successfully predict the location of major sediment-hosted Zn-Pb deposits while highlighting new areas of elevated prospectivity in under-explored regions of Australia, reducing the exploration search space by up to 85% for sediment-hosted Zn-Pb mineral systems.</div>

<div>Maps showing the potential for iron oxide copper-gold (IOCG) mineral systems in Australia. Each of the mineral potential maps is a synthesis of four component layers (source of metals, fluids and ligands; energy sources and fluid flow drivers; fluid flow pathways and architecture; and ore depositional gradients). The model uses a hybrid data-driven and knowledge driven methodology to produce the final mineral potential map for the mineral system. An uncertainty map is provided in conjunction with the mineral potential maps that represents the availability of data coverage over Australia for the selected combination of input maps. Uncertainty values range between 0 and 1, with higher uncertainty values being located in areas where more input maps are missing data or have unknown values. The input maps and mineral deposits and occurrences used to generate the mineral potential map are provided along with an assessment criteria table which contains information on the map creation.</div>

<div>Sediment-hosted copper (Cu) mineral systems are important sources of base metals and critical minerals such as cobalt that are vital to delivering Australia’s low-carbon economy. In Australia, sediment-hosted Cu resources account for ~11% of the total Cu resources. Given their significance to the Australian economy, national-scale mineral potential models for sediment-hosted Cu have been developed. In addition to the potential for sediment-hosted Cu mineralisation, the uncertainty related to data availability has been examined. Three mineral potential</div><div>models derived from the combination of two mineral systems have been derived from a large volume of precompetitive geoscience data combined with mineral systems expertise, each using a different combination of input maps to assess the influence of incomplete data on the results. The mineral potential models successfully predict the location of major sediment-hosted stratiform Cu and Mount Isa-type Cu deposits while highlighting new areas of elevated prospectivity in under-explored regions of Australia, reducing the exploration search space</div><div>by up to ~84%.</div>

<div>Mineral exploration and development involves the selection of potential projects which must be evaluated across disparate characteristics. However, the distinct metrics involved are typically difficult to reconcile (e.g. geological potential, environmental impact, jobs created, value generated, etc.). Separate stakeholders—with different goals and attitudes—will reasonably differ in their preferences as to which categories to prioritize and how much weight to give to each. These conflicting preferences can obscure optimal outcomes and confound project selection.</div><div><br></div><div>In this presentation, we will discuss how early-stage exploration decisions can be treated as multi-criteria optimization problems. We show how this approach can be used to effectively evaluate and communicate competing criteria, and locate regions that perform best under a range of different metrics. We then outline a mapping framework that identifies regions that perform best in terms of geological potential, economic value and environmental impact and demonstrate this approach in a real-word example that highlights new exploration targets in the Australian context. Abstract presented at the American Geophysical Union (AGU) Fall Meeting 2023 (AGU23) https://www.agu.org/fall-meeting

<div>The production of rare earth elements (REEs) is critical to the global transition to a low carbon economy. Carbonatites represent a significant source of REEs, both domestically within Australia, as well as globally. Given their strategic importance for the Australian economy, a national mineral potential assessment has been undertaken as part of the Exploring for the Future program at Geoscience Australia to evaluate the potential for carbonatite-related REE (CREE) mineral systems. Rather than aiming to identify individual carbonatites and/or CREE deposits, the focus of the mineral potential assessment is to delineate prospective belts or districts within Australia that indicate the presence of favourable criteria, particularly in terms of lithospheric architecture, that may lead to the formation of a CREE mineral system.</div><div><br></div><div>This study demonstrates how national-scale multidisciplinary precompetitive geoscience datasets can be integrated using a hybrid methodology that incorporates robust statistical analysis with mineral systems expertise to predictively map areas that have a higher geological potential for the formation of CREE mineral systems and effectively reduce the exploration search space. Statistical evaluation of the relationship between different mappable criteria that represent spatial proxies for mineral system processes and known carbonatites and CREE deposits has been undertaken to test previously published hypotheses on how to target CREE mineral systems at a broad-scale. The results confirm the relevance of most criteria in the Australian context, while several new criteria such as distance to large igneous province margins and distance to magnetic worms have also been shown to have a strong correlation with known carbonatites and CREE deposits. Using a hybrid knowledge- and data-driven mineral potential mapping approach, the mineral potential map predicts the location of known carbonatite and CREE deposits, while also demonstrating additional areas of high prospectivity in regions with no previously identified carbonatites or CREE mineralisation.</div> Presented at the AusIMM Critical Minerals Conference 2023.

<div>Mineral prospectivity studies seek to map evidence of mineral system activity, with the aim of informing mineral exploration decisions and guiding exploration in the face of uncertainty. These studies leverage the growing volumes of information that are available to characterise the lithosphere by compiling covariate (or feature) grids that represent key mineral system ingredients. Previous studies have been categorised as either “knowledge-driven” or “data-driven” approaches depending on whether these grids are integrated via expert elicitation or by the empirical relationship to known mineralisation, respectively. However, to our knowledge, the underlying modelling framework and assumptions have not been systematically reviewed to understand how choices in the approach to the problem influence modelling outcomes. Here we show the broad mathematical equivalence in these approaches and highlight the limitations inherent when optimising to minimise misfit in potentially under-determined problems. We argue that advances in mineral prospectivity are more likely to be driven by careful consideration of the model selection problem. Focusing effort on model selection will not only drive more robust mineral prospectivity predictions but may also simultaneously refine our understanding of key mineral system processes. To build on these results, we present the Mineral Potential Toolkit; a software repository to facilitate feature engineering, statistical appraisal, and quantitative prospectivity modelling. The toolkit enables a novel approach that combines the best aspects of previous methods. Abstract presented to the 26th World Mining Congress 2023 (https://wmc2023.org/)

<div>Maps showing the potential for carbonatite-related rare earth element (REE) mineral systems in Australia. Each of the mineral potential maps is a synthesis of three or four component layers. Model 1 integrates three components: sources of metals, energy drivers, and lithospheric architecture. Model 2 integrates four components: sources of metals, energy drivers, lithospheric architecture, and ore deposition. Both models use a hybrid data-driven and knowledge driven methodology to produce the final mineral potential map for the mineral system. An uncertainty map is provided in conjunction with the mineral potential map for Model 2 that represents the availability of data coverage over Australia for the selected combination of input maps. Uncertainty values range between 0 and 1, with higher uncertainty values being located in areas where more input maps are missing data or have unknown values. An assessment criteria table is provided and contains information on the map creation.</div>