Over 900 Australian mineral deposits, location and age data, combined with deposit classifications, have been used to assess temporal and spatial patterns of mineral deposits associated with convergent margins and allow assessment of the potential of poorly exposed or undercover mineral provinces and identification of prospective tracts within known mineral provinces. Here we present results of this analysis for the Eastern Goldfields Superterrane and the Tasman Element, which illustrate end-members of the spectrum of convergent margin metallogenic provinces. Combining our Australian synthesis with global data suggest that after ~3000 Ma these provinces are characterised by a reasonably consistent temporal pattern of deposit formation, termed the convergent margin metallogenic cycle (CMMC): volcanic-hosted massive sulfide – calc-alkalic porphyry copper – komatiite-associated nickel sulfide → orogenic gold → alkalic porphyry copper – granite-related rare metal (Sn, W and Mo) – pegmatite. Between ca 3000 Ma and ca 800 Ma, virtually all provinces are characterised by a single CMMC, but after ca 800 Ma, provinces mostly have multiple CMMCs. We interpret this change in metallogeny to reflect secular changes in tectonic style, with single-CMMC provinces associated with warm, shallow break-off subduction, and multiple-CMMC provinces associated with modern-style cold, deep break-off subduction. These temporal and spatial patterns can be used to infer potential for mineralisation outside well-established metallogenic tracts. <b>Citation:</b> Huston D. L., Doublier M. P., Eglington B., Pehrsson S., Mercier-Langevin P. & Piercey S., 2022. Convergent margin metallogenic cycling in the Eastern Goldfields Superterrane and Tasman Element. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/147037

Exploration and management of minerals, energy and groundwater resources requires robust constraints on subsurface geology. Over the last decade the passive seismic technique has grown in popularity as it is one of a handful of non-invasive methods of imaging the subsurface. Given regional imaging relies on comparing records of ground motion between simultaneous deployments of seismometers deployed for over a year, consistency and quality of data collection lies at the heart of this technique. Here, we summarise the standard operating procedures developed by Geoscience Australia over the last 6 years for deployment, servicing and retrieval of passive seismic arrays. Our purpose is to share our experience and thereby contribute to improving the quality of passive seismic data being acquired across Australia. <b>Citation:</b> Holzschuh J., Gorbatov A., Glowacki J., Cooper A. & Cooper C., 2022. AusArray temporary passive seismic station deployment, servicing and retrieval: Geoscience Australia standard operating procedures. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146999

The High Quality Geophysical Analysis (HiQGA) package is a fully-featured, Julia-language based open source framework for geophysical forward modelling, Bayesian inference, and deterministic imaging. A primary focus of the code is production inversion of airborne electromagnetic (AEM) data from a variety of acquisition systems. Adding custom AEM systems is simple using Julia’s multiple dispatch feature. For probabilistic spatial inference from geophysical data, only a misfit function needs to be supplied to the inference engine. For deterministic inversion, a linearisation of the forward operator (i.e., Jacobian) is also required. HiQGA is natively parallel, and inversions from a full day of production AEM acquisition can be inverted on thousands of CPUs within a few hours. This allows for quick assessment of the quality of the acquisition, and provides geological interpreters preliminary subsurface images of EM conductivity together with associated uncertainties. HiQGA inference is generic by design – allowing for the analysis of diverse geophysical data. Surface magnetic resonance (SMR) geophysics for subsurface water-content estimation is available as a HiQGA plugin through the SMRPInversion (SMR probabilistic inversion) wrapper. The results from AEM and/or SMR inversions are used to create images of the subsurface, which lead to the creation of geological models for a range of applications. These applications range from natural resource exploration to its management and conservation.

Heavy minerals (HMs) have been used successfully around the world in energy and mineral exploration, yet in Australia no public domain database or maps exist that document the background HM assemblages or distributions. Here, we describe a project that delivers the world’s first continental-scale HM maps. We applied automated mineralogical identification and quantification of the HMs contained in floodplain sediments from large catchments covering most of Australia. The composition of the sediments reflects the dominant rock types in each catchment, with the generally resistant HMs largely preserving the mineralogical fingerprint of their host protoliths through the weathering–transport–deposition cycle. Underpinning this vision was a pilot project, based on 10 samples from the national sediment sample archive, which in 2020 demonstrated the feasibility of a larger, national-scale project. Two tranches of the subsequent national HM dataset, one focusing on a 965,000 km2 region centred on Broken Hill in southeastern Australia, the other focusing on a 950,000 km2 area in northern Queensland and Northern Territory, were released in 2022. In those releases, over 47 million mineral grains were analysed in 411 samples, identifying over 150 HM species. We created a bespoke, cloud-based mineral network analysis (MNA) tool to visualize, explore and discover relationships between HMs as well as between them and geological settings or mineral deposits. We envisage that the Heavy Mineral Map of Australia and MNA tool, when released publicly by the end of 2023, will contribute significantly to mineral prospectivity analysis and modelling, particularly for technology critical elements and their host minerals <b>Citation:</b> Caritat P. de, Walker A.T., Bastrakov E. & McInnes B.I.A., 2023. From The Heavy Mineral Map of Australia: vision, implementation and progress. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/148678

The Exploring for the Future program Showcase 2023 was held on 15-17 August 2023. Day 2 - 16th August talks included: Highways to Discovery and Understanding Session AusAEM - Unraveling Australia's Landscape with Airborne Electromagnetics – Dr Yusen Ley Cooper Exploring for the Future Data Discovery Portal: A scenic tour – Simon van der Wielen Towards equitable access to regional geoscience information– Dr Kathryn Waltenberg Community engagement and geoscience knowledge sharing: towards inclusive national data and knowledge provision – Dr Meredith Orr Foundational Geoscience Session The power of national scale geological mapping – Dr Eloise Beyer New surface mineralogical and geochemical maps of Australia – Dr Patrice de Caritat Imaging Australia’s Lithospheric Architecture – Dr Babak Hejrani Metallogenic Potential of the Delamerian Margin– Dr Yanbo Cheng You can access the recording of the talks from YouTube here: <a href="https://youtu.be/ZPp2sv2nuXI">2023 Showcase Day 2 - Part 1</a> <a href="https://youtu.be/dvqP8Z5yVtY">2023 Showcase Day 2 - Part 2</a>

The Exploring for the Future program Showcase 2023 was held on 15-17 August 2023. Day 3 - 17th August talks included: Geological Processes and Resources Session Large scale hydrogen storage: The role of salt caverns in Australia’s transition to net zero – Dr Andrew Feitz Basin-Hosted Base Metal Deposits – Dr Evgeniy Bastrakov Upper Darling Floodplain: Groundwater dependent ecosystem assessment – Dr Sarah Buckerfield Atlas of Australian Mine Waste: Waste not, want not – Jane Thorne Resource Potential Theme National-scale mineral potential assessments: supporting mineral exploration in the transition to net zero – Dr Arianne Ford Australia’s Onshore Basin Inventories: Energy – Tehani Palu Prioritising regional groundwater assessments using the national hydrogeological inventory – Dr Steven Lewis Assessing the energy resources potential in underexplored regions – Dr Barry Bradshaw You can access the recording of the talks from YouTube here: <a href="https://youtu.be/pc0a7ArOtN4">2023 Showcase Day 3 - Part 1</a> <a href="https://youtu.be/vpjoVYIjteA">2023 Showcase Day 3 - Part 2</a>

The discovery of strategically located salt structures, which meet the requirements for geological storage of hydrogen, is crucial to meeting Australia’s ambitions to become a major hydrogen producer, user and exporter. The use of the AusAEM airborne electromagnetic (AEM) survey’s conductivity sections, integrated with multidisciplinary geoscientific datasets, provides an excellent tool for investigating the near-surface effects of salt-related structures, and contributes to assessment of their potential for underground geological hydrogen storage. Currently known salt in the Canning Basin includes the Mallowa and Minjoo salt units. The Mallowa Salt is 600-800 m thick over an area of 150 × 200 km, where it lies within the depth range prospective for hydrogen storage (500-1800 m below surface), whereas the underlying Minjoo Salt is generally less than 100 m thick within its much smaller prospective depth zone. The modelled AEM sections penetrate to ~500 m from the surface, however, the salt rarely reaches this level. We therefore investigate the shallow stratigraphy of the AEM sections for evidence of the presence of underlying salt or for the influence of salt movement evident by disruption of near-surface electrically conductive horizons. These horizons occur in several stratigraphic units, mainly of Carboniferous to Cretaceous age. Only a few examples of localised folding/faulting have been noted in the shallow conductive stratigraphy that have potentially formed above isolated salt domes. Distinct zones of disruption within the shallow conductive stratigraphy generally occur along the margins of the present-day salt depocentre, resulting from dissolution and movement of salt during several stages. This study demonstrates the potential AEM has to assist in mapping salt-related structures, with implications for geological storage of hydrogen. In addition, this study produces a regional near-surface multilayered chronostratigraphic interpretation, which contributes to constructing a 3D national geological architecture, in support of environmental management, hazard mapping and resource exploration. <b>Citation: </b>Connors K. A., Wong S. C. T., Vilhena J. F. M., Rees S. W. & Feitz A. J., 2022. Canning Basin AusAEM interpretation: multilayered chronostratigraphic mapping and investigating hydrogen storage potential. In: Czarnota, K (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146376

<div>A national compilation of airborne electromagnetic (AEM) conductivity–depth models from AusAEM (Ley-Cooper et al. 2020) survey line data and other surveys (see reference list in the attachments) has been used to train a conductivity model prediction for the 0-4 m and 30 m depth intervals. Over 460,000 training points/measurements were used in a 5 K-Fold training and validation split. A further 28,626 points/measurements were used to assess the out of sample performance (OOS; i.e. points not used in the model validation). Modelling of the conductivity values (i.e. measurements along the AEM survey lines) was performed using the gradient boosted (GB) tree algorithm. The GB model is a machine learning (ML) ensemble technique used for both regression and classification tasks (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html). Samples along the flight-line were thinned to approximately one sample per 300 m. This avoided the situation where we could have more than one sample per pixel (i.e. features or covariates used in the model prediction have a cell or pixel size of 80 m) that could otherwise lead to over fitting. In addition, out of sample set used label clusters or groups to minimise overfitting. Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th respectively) to measure the model uncertainty. Grids show conductivity (S/m) in log 10 units. The methodology used to generate these conductivity grids are overall similar to that described by Wilford, et al. 2022.</div><div>&nbsp;</div><div>Reported out-of-sample r-squares for the 0-4 m and 3 m depths are 0.76 and 0.74, respectively. The ML approach allows estimation of conductivity into areas where we do not have airborne electromagnetic survey coverage. Hence these model have a national extent. Where we do not have AEM survey coverage the model is finding relationships with the covariates and making informed estimates of conductivity in those areas. Where those relationships are not well understood (i.e. where we see a departure in the feature space characteristics from what the model can ‘see’) the model prediction is likely to be less certain. Differences in the features and their corresponding values ‘seen’ and used in the model versus the full feature space covering the entire continent are captured in the covariate shift map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map.</div><div>&nbsp;</div><div>Datasets in this data package include:</div><div>&nbsp;</div><div>1. 0_4m_conductivity_prediction_median.tif</div><div>2. 0_4m_conductivity_lower_percentile_5th.tif</div><div>3. 0_4m_conductivity_upper_percentile_95th.tif</div><div>4. 30m_conductivity_prediction_median.tif</div><div>5.30m_conductivity_lower_percentile_5th.tif</div><div>6. 30m_conductivity_upper_percentile_95th.tif</div><div>7. National_conductivity_model_shift.tif</div><div>8. Full list of referenced AEM survey datasets used to train the model (word document)</div><div>9. Map showing the distribution of training and out-of-sample sites</div><div><br></div><div>All the Geotiffs (1-6) are in log (10) electrical conductivity siemens per metre (S/m).</div><div>&nbsp;</div><div>This work is part of Geoscience Australia’s Exploring for the Future program which provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div><div><br></div><div><br></div><div><strong>Reference:</strong></div><div><br></div><div>Ley-Cooper, A. Y., Brodie, R.C., and Richardson, M. 2020. AusAEM: Australia’s airborne electromagnetic continental-scale acquisition program, Exploration Geophysics, 51:1, 193-202, DOI: 10.1080/08123985.2019.1694393</div><div><br></div><div>Wilford, J., LeyCooper, Y., Basak, S., Czarnota, K. 2022. High resolution conductivity mapping using regional AEM survey and machine learning. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/146380</div>

<div>Airborne electromagnetics surveys are at the forefront of addressing the challenge of exploration undercover. They have been essential in the regional mapping programmes to build Australia's resource potential inventory and provide information about the subsurface. In collaboration with state and territory geological surveys, Geoscience Australia (GA) leads a national initiative to acquire AEM data across Australia at 20 km line spacing, as a component of the Australian government Exploring for The Future (EFTF) program. Regional models of subsurface electrical conductivity show new undercover geological features that could host critical mineral deposits and groundwater resources. The models enable us to map potential alteration and structural zones and support environmental and land management studies. Several features observed in the AEM models have also provided insights into possible salt distribution analysed for its hydrogen storage potential. The AusAEM programme is rapidly covering areas with regional AEM transects at a scale never previously attempted. The programme's success leans on the high-resolution, non-invasive nature of the method and its ability to derive subsurface electrical conductivity in three dimensions – made possible by GA's implementation of modern high-performance computing algorithms. The programme is increasingly acquiring more AEM data, processing it, and working towards full national coverage.</div> This Abstract was submitted/presented to the 2023 Australian Exploration Geoscience Conference 13-18 Mar (https://2023.aegc.com.au/)

Demand for critical minerals, vital for advanced technologies, is increasing. This study shows that Australia’s richly endowed geological provinces contain numerous undeveloped or abandoned mineral occurrences that could potentially lead to new economic resources. Three study areas were assessed for critical mineral occurrences through database interrogation and literature review, namely the Barkly-Isa-Georgetown (BIG), Darling-Curnamona-Delamerian (DCD) and Officer-Musgrave (OM) project areas. The study found approximately 20,000 mineral occurrences across the three areas, with just over half occurring in the DCD region. Critical minerals were recognised in ~10% of all occurrences in BIG, ~10% in DCD and 70% in OM. Gold and base metal occurrences comprise 48% (OM), 81% (DCD) and 82% (BIG) of all occurrences in the study areas, with these metals in the DCD and BIG historically and presently important. This large-scale analysis and literature review of Australia’s forgotten mineral discoveries identifies potential new sources of critical minerals and, with the addition of mineralisation style to the data, contributes to predictive exploration methodology that will further unlock the nation’s critical mineral potential. These data are available through the Exploring for the Future portal (https://portal.ga.gov.au/persona/eftf). <b>Citation:</b> Kucka C., Senior A. & Britt A., 2022. Mineral Occurences: Forgotten discoveries providing new leads for mineral supply. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146983