<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>

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>

<div>The lithology, geochemistry, and architecture of the continental lithospheric mantle (CLM) underlying the Kimberley Craton of north-western Australia has been constrained using pressure-temperature estimates and mineral compositions for &gt;5,000 newly analyzed and published garnet and chrome (Cr) diopside mantle xenocrysts from 25 kimberlites and lamproites of Mesoproterozoic to Miocene age. Single-grain Cr diopside paleogeotherms define lithospheric thicknesses of 200–250 km and fall along conductive geotherms corresponding to a surface heat flow of 37–40 mW/m 2. Similar geotherms derived from Miocene and Mesoproterozoic intrusions indicate that the lithospheric architecture and thermal state of the CLM has remained stable since at least 1,000 Ma. The chemistry of xenocrysts defines a layered lithosphere with lithological and geochemical domains in the shallow (&lt;100 km) and deep (&gt;150 km) CLM, separated by a diopside-depleted and seismically slow mid-lithosphere discontinuity (100–150 km). The shallow CLM is comprised of Cr diopsides derived from depleted garnet-poor and spinel-bearing lherzolite that has been weakly metasomatized. This layer may represent an early (Meso to Neoarchean?) nucleus of the craton. The deep CLM is comprised of high Cr2O3 garnet lherzolite with lesser harzburgite, and eclogite. The peridotite components are inferred to have formed as residues of polybaric partial mantle melting in the Archean, whereas eclogite likely represents former oceanic crust accreted during Paleoproterozoic subduction. This deep CLM was metasomatized by H2O-rich melts derived from subducted sediments and high-temperature FeO-TiO2 melts from the asthenosphere.</div><div><br></div><div>Geoscience Australia’s Exploring for the Future program 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><strong>Citation:</strong></div><div>Sudholz, Z.J., et al. (2023) Mapping the Structure and Metasomatic Enrichment of the Lithospheric Mantle Beneath the Kimberley Craton, Western Australia,&nbsp;<em><i>Geochemistry, Geophysics, Geosystems</i>,</em>&nbsp;24, e2023GC011040.</div><div>https://doi.org/10.1029/2023GC011040</div>

Maps showing the potential for sediment-hosted base metal mineral systems in Australia. Each of the mineral potential maps is a synthesis of four component layers: sources of metals, energy drivers, lithospheric architecture, and depositional gradients, using a weighted sum to produce the final mineral potential map for the mineral system. Uncertainty maps are provided in conjunction with each of the mineral potential maps that represent 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 set of input maps used to generate the mineral potential maps is provided along with an assessment criteria table that contains information on the map creation.

<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 contribution presents the distribution and geology of Australian alkaline and related rocks of Paleozoic age, one in a series within the Alkaline Rocks Atlas of Australia that collectively document alkaline rocks across the continent through time. </div><div><br></div><div>In general, alkaline and related rocks are a relatively rare class of igneous rocks worldwide. Alkaline rocks encompass a wide range of rock types and are mineralogically and geochemically diverse. They are typically thought to have been derived by generally small to very small degrees of partial melting of a wide range of mantle compositions. As such these rocks have the potential to convey considerable information on the evolution of the Earth’s mantle (asthenosphere and lithosphere), particularly the role of metasomatism, which may have been important in their generation, or to which such rocks may themselves have contributed. Such rocks, by their unique compositions and/or enrichments in their source protoliths, also have considerable metallogenic potential, e.g., diamonds, Th, U, Zr, Hf, Nb, Ta, REEs. It is evident that the geographic occurrences of many of these rock types are also important, and may relate to presence of old cratons, craton margins or major lithospheric breaks. Finally, many alkaline rocks also carry with them mantle xenoliths providing a snapshot of the lithospheric mantle composition at the time of their emplacement.</div><div><br></div><div>Accordingly, although alkaline and related rocks comprise only a volumetrically minor component of the geology of Australia, they are of considerable importance to studies of lithospheric composition, evolution and architecture and to helping constrain the temporal evolution of the lithosphere. They are also directly related to metallogenesis and mineralisation, particularly for a number of the critical minerals, e.g., rare earth elements, niobium. In light of this, Geoscience Australia is undertaking a compilation of the distribution and geology of Australian alkaline and related rocks, of all ages, and producing a GIS and associated database of such rocks, to both document such rocks and for use in metallogenic and mineral potential studies.&nbsp;</div><div><br></div><div>The broadening of the definition of alkaline rocks within the Alkaline Rocks Atlas herein, to include ultra-high K mafic to felsic silica-saturated rocks (alkaline-shoshonites), which are commonly formed at convergent margin settings, manifests in some divergences in the presentation of alkaline rocks that are particularly relevant to the Phanerozoic, and Paleozoic Australia in particular.&nbsp;</div><div><br></div><div>Paleozoic alkaline and related rocks occur throughout eastern Australia, with occurrences in the Northern Territory, and in all States excluding Western Australia. However, with a few exceptions they are principally located within the Tasman Element, and are over-represented in NSW – with respect to other states jurisdictions (based on available data). Paleozoic alkaline rocks range from ultramafic through to felsic, and from strongly alkaline (undersaturated) through to mildly alkaline.&nbsp;</div><div><br></div><div>Strongly alkaline rocks – congruent with the outline of alkaline rocks presented above – are comparatively rare in the Paleozoic, and are compositionally diverse incorporating alkali basalt, kimberlite, carbonatite-related rocks, and lamprophyre, with wide-ranging ages.&nbsp;</div><div><br></div><div>Overwhelmingly, the Paleozoic alkaline rock compilation is dominated by very high K alkali mafic to felsic silica-saturated rocks. Mafic-intermediate rocks within this grouping typically have an “arc signature” (i.e., low Nb/Y) but incorporate both arc magmas as well as rocks associated with backarc rifting. These rocks typically occur within rock units or packages that comprise a diverse array of rock types and compositions from volcanic rocks, related volcaniclastics and epiclastics through to sedimentary rocks. Igneous rocks within these packages commonly range from subalkaline / calc-alkaline through to mildly alkaline (trachybasalt to trachyandesite, and less commonly trachyte) based on alkali contents. Quartz-saturated felsic alkaline rocks are dominated by near peralkaline–peralkaline A-types and high-temperature transitional I-A compositions, but locally include rarer mildly alkaline (based on HFSE) rocks. The inclusion of whole rock units, which may only incorporate a small volume of alkaline rocks, necessarily means that the volume of these alkaline rocks is both poorly constrained and over-represented with this dataset.</div><div><br></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/)

The Exploring for the Future program Showcase 2023 was held on 15-17 August 2023. Day 1 - 15th August talks included: Resourcing net zero – Dr Andrew Heap Our Geoscience Journey – Dr Karol Czarnota You can access the recording of the talks from YouTube here: <a href="https://youtu.be/uWMZBg4IK3g">2023 Showcase Day 1</a>

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.