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>

Improvements in discovery and management of minerals, energy and groundwater resources are spurred along by advancements in surface and subsurface imaging of the Earth. Over the last half decade Australia has led the world in the collection of regionally extensive airborne electromagnetic (AEM) data coverage, which provides new constraints on subsurface conductivity structure. Inferring geology and hydrology from conductivity is non-trivial as the conductivity response of earth materials is non-unique, but careful calibration and interpretation does provide significant insights into the subsurface. To date utility of this new data is limited by its spatial extent. The AusAEM survey provides conductivity constraints every 12.5 m along flight lines with no constraints across vast areas between flight lines spaced 20 km apart. Here we provide a means to infer the conductivity between flight lines as an interim measure before infill surveys can be undertaken. We use a gradient boosted tree machine learning algorithm to discover relationships between AEM conductivity models across northern Australia and other national data coverages for three depth ranges: 0–0.5 m, 9–11 m and 22–27 m. The predictive power of our models decreases with depth but they are nevertheless consistent with our knowledge of geological, landscape evolution and climatic processes and an improvement on standard interpolation methods such as kriging. Our models provide a novel complementary methodology to gridding/interpolating from AEM conductivity alone for use by the mining, energy and natural resource management sectors. <b>Citation: </b>Wilford J., Ley-Cooper Y., Basak S., & Czarnota K., 2022. High resolution conductivity mapping using regional AEM survey and machine learning. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146380.

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.

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 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

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

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

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

<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>This data package contains interpretations of airborne electromagnetic (AEM) conductivity sections in the Exploring for the Future (EFTF) program’s Eastern Resources Corridor (ERC) study area, in south eastern Australia. Conductivity sections from 3 AEM surveys were interpreted to provide a continuous interpretation across the study area – the EFTF AusAEM ERC (Ley-Cooper, 2021), the Frome Embayment TEMPEST (Costelloe et al., 2012) and the MinEx CRC Mundi (Brodie, 2021) AEM surveys. Selected lines from the Frome Embayment TEMPEST and MinEx CRC Mundi surveys were chosen for interpretation to align with the 20&nbsp;km line-spaced EFTF AusAEM ERC survey (Figure 1).</div><div>The aim of this study was to interpret the AEM conductivity sections to develop a regional understanding of the near-surface stratigraphy and structural architecture. To ensure that the interpretations took into account the local geological features, the AEM conductivity sections were integrated and interpreted with other geological and geophysical datasets, such as boreholes, potential fields, surface and basement geology maps, and seismic interpretations. This approach provides a near-surface fundamental regional geological framework to support more detailed investigations. </div><div>This study interpreted between the ground surface and 500&nbsp;m depth along almost 30,000 line kilometres of nominally 20&nbsp;km line-spaced AEM conductivity sections, across an area of approximately 550,000&nbsp;km2. These interpretations delineate the geo-electrical features that correspond to major chronostratigraphic boundaries, and capture detailed stratigraphic information associated with these boundaries. These interpretations produced approximately 170,000 depth estimate points or approximately 9,100 3D line segments, each attributed with high-quality geometric, stratigraphic, and ancillary data. The depth estimate points are formatted for compliance with Geoscience Australia’s (GA) Estimates of Geological and Geophysical Surfaces (EGGS) database, the national repository for standardised depth estimate points. </div><div>Results from these interpretations provided support to stratigraphic drillhole targeting, as part of the Delamerian Margins NSW National Drilling Initiative campaign, a collaboration between GA’s EFTF program, the MinEx CRC National Drilling Initiative and the Geological Survey of New South Wales. The interpretations have applications in a wide range of disciplines, such as mineral, energy and groundwater resource exploration, environmental management, subsurface mapping, tectonic evolution studies, and cover thickness, prospectivity, and economic modelling. It is anticipated that these interpretations will benefit government, industry and academia with interest in the geology of the ERC region.</div>