<div>High Purity Silica (HPS) is the principal raw material in the production of silicon used to manufacture high technology products including semiconductors and solar cells. Quartz (SiO2) is the most abundant silica mineral in the Earth’s crust; however, economic deposits of high purity quartz (HPQ; SiO2 >99.995%) are rare. Rapid acceleration towards reaching net zero emissions has seen a parallel increase in demand for the discovery of new HPS deposits for downstream processing. As a part of the Australian Critical Minerals Research and Development Hub, Geoscience Australia is addressing this demand by generating the first mineral systems model and accompanying national scale mineral potential map to help explorers accelerate discovery. Presentation for the 2024 AusIMM Critical Minerals Conference

This web service delivers data from an aggregation of sources, including several Geoscience Australia databases (provinces (PROVS), mineral resources (OZMIN), energy systems (AERA, ENERGY_SYSTEMS) and water (HYDROGEOLOGY). Information is grouped based on a modified version of the Australian Bureau of Statistics (ABS) 2021 Indigenous Regions (IREG). Data covers population centres, top industries, a regional summary, groundwater resources and uses, energy production and potential across six sources and two energy storage options. Mineral production and potential covers 36 commodities that are grouped into 13 groups.

This web service delivers data from an aggregation of sources, including several Geoscience Australia databases (provinces (PROVS), mineral resources (OZMIN), energy systems (AERA, ENERGY_SYSTEMS) and water (HYDROGEOLOGY). Information is grouped based on a modified version of the Australian Bureau of Statistics (ABS) 2021 Indigenous Regions (IREG). Data covers population centres, top industries, a regional summary, groundwater resources and uses, energy production and potential across six sources and two energy storage options. Mineral production and potential covers 36 commodities that are grouped into 13 groups.

Rapid, efficient, and accurate prediction of mineral occurrence that takes uncertainty into 20 account is essential to optimise defining exploration targets. Traditional approaches to mineral 21 potential mapping often fail to fully appreciate spatial uncertainties of input predictors and their 22 spatial cross-correlation. In this study a stochastic technique based on multivariate 23 geostatistical simulations and ensemble tree-based learners is introduced for predicting and 24 uncertainty quantification of mineral exploration targets. The technique is tested on a synthetic 25 case inspired by the characteristics of a hydrothermal mineral system model and a real-world 26 dataset from the Yilgarn Craton in Western Australia. Results from the two cases proved the 27 superior performance and robustness of the proposed stochastic technique, especially when 28 dealing with high dimensional and large data sets. <b>Citation:</b> Talebi, H., Mueller, U., Peeters, L.J.M. et al. Stochastic Modelling of Mineral Exploration Targets. <i>Math Geosci </i>54, 593–621 (2022). https://doi.org/10.1007/s11004-021-09989-z

This web service delivers data from an aggregation of sources, including several Geoscience Australia databases (provinces (PROVS), mineral resources (OZMIN), energy systems (AERA, ENERGY_SYSTEMS) and water (HYDROGEOLOGY). Information is grouped based on a modified version of the Australian Bureau of Statistics (ABS) 2021 Indigenous Regions (IREG). Data covers population centres, top industries, a regional summary, groundwater resources and uses, energy production and potential across six sources and two energy storage options. Mineral production and potential covers 36 commodities that are grouped into 13 groups.

<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>

The Exploring for the Future program Showcase 2024 was held on 13-16 August 2024. Day 3 - 15th August talks included: <b>Session 1 – Hydrogen opportunities across Australia</b> <a href="https://youtu.be/pA9ft3-7BtU?si=V0-ccAmHHIYJIZAo">Hydrogen storage opportunities and the role of depleted gas fields</a> - Dr Eric Tenthorey <a href="https://youtu.be/MJFhP57nnd0?si=ECO7OFTCak78Gn1M">The Green Steel Economic Fairways Mapper</a> - Dr Marcus Haynes <a href="https://youtu.be/M95FOQMRC7o?si=FyP7CuDEL0HEdzPw">Natural hydrogen: The Australian context</a> - Chris Boreham <b>Session 2 – Sedimentary basin resource potential – source rocks, carbon capture and storage (CCS) and groundwater</b> <a href="https://youtu.be/44qPlV7h3os?si=wfQqxQ81Obhc_ThE">Australian Source Rock and Fluid Atlas - Accessible visions built on historical data archives</a> - Dr Dianne Edwards <a href="https://youtu.be/WcJdSzsADV8?si=aH5aYbpnjaz3Qwj9">CO2: Where can we put it and how much will it cost?</a> - Claire Patterson <a href="https://youtu.be/Y8sA-iR86c8?si=CUsERoEkNDvIwMtc">National aquifer framework: Putting the geology into hydrogeology</a> - Dr Nadege Rollet <b>Session 3 – Towards a national inventory of resource potential and sustainable development</b> <a href="https://youtu.be/K5xGpwaIWgg?si=2s0AKuNpu30sV1Pu">Towards a national inventory of mineral potential</a> - Dr Arianne Ford <a href="https://youtu.be/XKmEXwQzbZ0?si=yAMQMjsNCGkAQUMh">Towards an inventory of mine waste potential</a> - Dr Anita Parbhakar-Fox <a href="https://youtu.be/0AleUvr2F78?si=zS4xEsUYtARywB1j">ESG mapping of the Australian mining sector: A critical review of spatial datasets for decision making</a> - Dr Eleonore Lebre View or download the <a href="https://dx.doi.org/10.26186/149800">Exploring for the Future - An overview of Australia’s transformational geoscience program</a> publication. View or download the <a href="https://dx.doi.org/10.26186/149743">Exploring for the Future - Australia's transformational geoscience program</a> publication. You can access full session and Q&A recordings from YouTube here: 2024 Showcase Day 3 - Session 1 - <a href="https://www.youtube.com/watch?v=Ho6QFMIleuE">Hydrogen opportunities across Australia</a> 2024 Showcase Day 3 - Session 2 - <a href="https://www.youtube.com/watch?v=ePZfgEwo0m4">Sedimentary basin resource potential – source rocks, carbon capture and storage (CCS) and groundwater</a> 2024 Showcase Day 3 - Session 3 - <a href="https://www.youtube.com/watch?v=CjsZVK4h6Dk">Towards a national inventory of resource potential and sustainable development</a>

This web service provides access to datasets produced by the mineral potential assement of iron oxide-copper-gold (IOCG) mineral systems in the Tennant Creek – Mt Isa region. The mineral potential assessment uses a 2D, GIS-based workflow to qualitatively map four key mineral system components: (1) Sources of metals, fluids and ligands, (2) Energy to drive fluid flow, (3) Fluid flow pathways and architecture, and (4) Deposition mechanisms, such as redox or chemical gradients. For each of these key mineral system components theoretical criteria, representing important ore-forming processes, were identified and translated into mappable proxies using a wide range of input datasets. Each of these criteria are weighted and combined using an established workflow to produce the final map of IOCG potential.

This web service provides access to datasets produced by the mineral potential assement of iron oxide-copper-gold (IOCG) mineral systems in the Tennant Creek – Mt Isa region. The mineral potential assessment uses a 2D, GIS-based workflow to qualitatively map four key mineral system components: (1) Sources of metals, fluids and ligands, (2) Energy to drive fluid flow, (3) Fluid flow pathways and architecture, and (4) Deposition mechanisms, such as redox or chemical gradients. For each of these key mineral system components theoretical criteria, representing important ore-forming processes, were identified and translated into mappable proxies using a wide range of input datasets. Each of these criteria are weighted and combined using an established workflow to produce the final map of IOCG potential.

In the first half of 2019, a collaborative mineral potential mapping project was undertaken between the Geological Survey of New South Wales (GSNSW) and Kenex to examine the mineral potential in the eastern Lachlan Orogen (ELO; Ford et al., 2019b). This project was part of a broader state-wide study that utilised the high quality publicly available geoscience data provided by the GSNSW to generate data-driven mineral potential maps using the weights of evidence (WofE) technique for different mineral systems in key metallogenic districts within NSW (Ford et al., 2019a). The aim of this collaborative project was to deliver a product that could be used to provide justifiable land use planning advice to key government stakeholders, as well as to highlight the exploration potential for key mineral systems at a regional scale. One key mineral system that was included in the 2019 ELO study was the porphyry Cu-Au mineral system, which was constrained to the Macquarie Arc. The results of the WofE mineral potential mapping for this porphyry model were broadly successful in terms of predicting the location of both the training data used in the WofE model, as well as a separate set of validation porphyry Cu-Au occurrences. However, the model failed to predict the location of one of the training points, Kaiser, in the prospective area. This failure to predict Kaiser led to a re-evaluation of the data using a variety of different machine learning techniques, in particular random forests (RF; Ford, 2020) and neural networks (NN). No additional or updated data was incorporated, and the maps used in the machine learning were the same maps made as part of the initial WofE study in 2019. The results show that the use of input maps that have been pre-classified to determine optimal thresholds outperform input maps that have had no favourability criteria applied when typical benchmarks for exploration targeting are considered. In addition, the NN analysis shows strong evidence of overfitting to the training data when a large number of input maps are used. A moderate degree of success for targeting under cover was achieved when only geophysical maps were included in the models. Abstract presented at the 8th Mines & Wines Conference 2022 (https://www.aig.org.au/events/8th-mines-wines-conference-2022/)