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

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

<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>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 though 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 enriched 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, as well as more directly to metallogenesis and mineralisation.</div><div><br></div><div>This contribution presents data on the distribution and geology of Australian alkaline and related rocks of Proterozoic age. Proterozoic alkaline and related rocks are primarily restricted to the western two-thirds of the Australia continent, congruent with the distribution of Proterozoic rocks more generally. Proterozoic alkaline rock units are most abundant in Western Australia and the Northern Territory, with minor occurrences in South Australia, and the western regions of Queensland, New South Wales and Tasmania.</div><div><br></div><div>The report and accompanying GIS document the distribution, age, lithology, mineralogy and other characteristics of these rocks (e.g., extrusive/intrusive, presence of mantle xenoliths, presence of diamonds), as well as references for data sources and descriptions. The report also reviews the nomenclature of alkaline rocks and classification procedures. GIS metadata are documented in the appendices.&nbsp;</div>

Preamble: The 'National Geochemical Survey of Australia: The Geochemical Atlas of Australia' was published in July 2011 along with a digital copy of the NGSA geochemical dataset (http://dx.doi.org/10.11636/Record.2011.020). The NGSA project is described here: www.ga.gov.au/ngsa. The present dataset contains additional geochemical data obtained on NGSA samples: the Lead Isotopes Dataset. Abstract: Over 1200 new lead (Pb) isotope analyses were obtained on catchment outlet sediment samples from the NGSA regolith archive to document the range and patterns of Pb isotope ratios in the surface regolith and their relationships to geology and anthropogenic activity. The selected samples included 1204 NGSA Top Outlet Sediment (TOS) samples taken from 0 to 10 cm depth and sieved to <2 mm (or ‘coarse’ fraction); three of these were analysed in duplicate for a total of 1207 Pb isotope analyses. Further, 12 Northern Australia Geochemical Survey (NAGS; http://dx.doi.org/10.11636/Record.2019.002) TOS samples from within a single NGSA catchment, also sieved to <2 mm, were analysed to provide an indication of smaller scale variability. Combined, we thus present 1219 new TOS coarse, internally comparable data points, which underpin new national regolith Pb isoscapes. Additionally, 16 NGSA Bottom Outlet Sediment (BOS; ~60 to 80 cm depth) samples, also sieved to <2 mm, and 16 NGSA TOS samples sieved to a finer grainsize (<75 um, or ‘fine’) fraction from selected NGSA catchments were also included to inform on Pb mobility and residence. Lead isotope analyses were performed by Candan Desem as part of her PhD research at the School of Geography, Earth and Atmospheric Sciences, University of Melbourne. After an initial ammonium acetate (AmAc) leach, the samples were digested in aqua regia (AR). Although a small number of samples were analysed after the AmAc leach, all samples were analysed after the second, AR digestion, preparation step. The analyses were performed without prior matrix removal using a Nu Instruments Attom single collector Sector Field-Inductively Coupled Plasma-Mass Spectrometer (SF-ICP-MS). The dried soil digests were redissolved in 2% HNO3 run solutions containing high-purity thallium (1 ppb Tl) and diluted to provide ~1 ppb Pb in solution. Admixture of natural, Pb-free Tl (with a nominal 205Tl/203Tl of 2.3871) allowed for correction of instrumental mass bias effects. Concentrations of matrix elements in the diluted AR digests are estimated to be in the range of 1–2 ppm. The SF-ICP-MS was operated in wet plasma mode using a Glass Expansion cyclonic spray chamber and glass nebuliser with an uptake rate of 0.33 mL/min. The instrument was tuned for maximum sensitivity and provided ~1 million counts per second/ppb Pb while maintaining flat-topped peaks. Each analysis, performed in the Attom’s ‘deflector peak jump’ mode, consists of 30 sets of 2000 sweeps of masses 202Hg, 203Tl, 204Pb, 205Tl, 206Pb, 207Pb and 208Pb, with dwell times of 500 μs and a total analysis time of 4.5 min. Each sample acquisition was preceded by a blank determination. All corrections for baseline, sample Hg interference (202Hg/204Pb ratios were always <0.043) and mass bias were performed online, producing typical in-run precisions (2 standard errors) of ±0.047 for 206Pb/204Pb, ±0.038 for 207Pb/204Pb, ±0.095 for 208Pb/204Pb, ±0.0012 for 207Pb/206Pb and ±0.0026 for 208Pb/206Pb. A small number of samples with low Pb concentrations exhibited very low signal sizes during analysis resulting in correspondingly high analytical uncertainties. Samples producing within-run uncertainties of >1% relative (measured on the 207Pb/204Pb ratio) were discarded as being insufficiently precise to contribute meaningfully to the dataset. Data quality was monitored using interspersed analyses of Tl-doped ~1 ppb solutions of the National Institute of Standards and Technology (NIST) SRM981 Pb standard, and several silicate reference materials: United States Geological Survey ‘BCR-2’ and ‘AGV-2’, Centre de Recherches Pétrographiques et Géochimiques ‘BR’ and Japan Geological Survey ‘JB-2’. In a typical session, up to 50 unknowns plus 15 standards were analysed using an ESI SC-2 DX autosampler. Although previous studies using the Attom SF-ICP-MS used sample-standard-bracketing techniques to correct for instrumental Pb mass bias, Tl doping was found to produce precise, accurate and reproducible results. Based upon the data for the BCR-2 and AGV-2 secondary reference materials, for which we have the most analyses, deviations from accepted values (accuracy) were typically <0.17%. Data for the remaining silicate standards appear slightly less accurate but these results may, to some extent, reflect uncertainty in the assigned literature values for these materials. Replicate runs of selected AR digests yielded similar reproducibility estimates. The results show a wide range of Pb isotope ratios in the NGSA (and NAGS) TOS <2 mm fraction samples across the continent (N = 1219): 206Pb/204Pb: Min = 15.558; Med ± Robust SD = 18.844 ± 0.454; Mean ± SD = 19.047 ± 1.073; Max = 30.635 207Pb/204Pb; Min = 14.358; Med ± Robust SD = 15.687 ± 0.091; Mean ± SD = 15.720 ± 0.221; Max = 18.012 208Pb/204Pb; Min = 33.558; Med ± Robust SD = 38.989 ± 0.586; Mean ± SD = 39.116 ± 1.094; Max = 48.873 207Pb/206Pb; Min = 0.5880; Med ± Robust SD = 0.8318 ± 0.0155; Mean ± SD = 0.8270 ± 0.0314; Max = 0.9847 208Pb/206Pb; Min = 1.4149; Med ± Robust SD = 2.0665 ± 0.0263; Mean ± SD = 2.0568 ± 0.0675; Max = 2.3002 These data allow the construction of the first continental-scale regolith Pb isotope maps (206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 207Pb/206Pb, and 208Pb/206Pb isoscapes) of Australia and can be used to understand contributions of Pb from underlying bedrock (including Pb-rich mineralisation), wind-blown dust and possibly from anthropogenic sources (industrial, transport, agriculture, residential, waste handling). The complete dataset is available to download as a comma separated values (CSV) file from Geoscience Australia's website (http://dx.doi.org/10.26186/5ea8f6fd3de64). Isoscape grids (inverse distance weighting interpolated grids with a power coefficient of 2 prepared in QGis using GDAL gridding tool based on nearest neighbours) are also provided for the five Pb isotope ratios (IDW2NN.TIF files in zipped folder). Alternatively, the new Pb isotope data can be downloaded from and viewed on the GA Portal (https://portal.ga.gov.au/).

<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>

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>We present the first national-scale lead (Pb) isotope maps of Australia based on surface regolith for five isotope ratios, ²⁰⁶Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁴Pb, ²⁰⁸Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁶Pb, and ²⁰⁸Pb/²⁰⁶Pb, determined by single collector Sector Field-Inductively Coupled Plasma-Mass Spectrometry after an Ammonium Acetate leach followed by Aqua Regia digestion. The dataset is underpinned principally by the National Geochemical Survey of Australia (NGSA) archived floodplain sediment samples. We analysed 1219 ‘top coarse’ (0-10 cm depth, &lt;2 mm grain size) samples, collected near the outlet of 1098 large catchments covering 5.647 million km2 (~75% of Australia). This paper focusses on the Aqua Regia dataset. The samples consist of mixtures of the dominant soils and rocks weathering in their respective catchments (and possibly those upstream) and are therefore assumed to form a reasonable representation of the average isotopic signature of those catchments. This assumption was tested in one of the NGSA catchments, within which 12 similar ‘top coarse’ samples were also taken; results show that the Pb isotope ratios of the NGSA catchment outlet sediment sample are close to the average of the 12 sub-catchment, upstream samples. National minimum, median and maximum values reported for ²⁰⁶Pb/²⁰⁴Pb were 15.558, 18.844, 30.635; for ²⁰⁷Pb/²⁰⁴Pb 14.358, 15.687, 18.012; for ²⁰⁸Pb/²⁰⁴Pb 33.558, 38.989, 48.873; for ²⁰⁷Pb/²⁰⁶Pb 0.5880, 0.8318, 0.9847; and for ²⁰⁸Pb/²⁰⁶Pb 1.4149, 2.0665, 2.3002, respectively. The new dataset was compared with published bedrock and ore Pb isotope data, and was found to dependably represent crustal elements of various ages from Archean to Phanerozoic. This suggests that floodplain sediment samples are a suitable proxy for basement and basin geology at this scale, despite various degrees of transport, mixing, and weathering experienced in the regolith environment, locally over protracted periods of time. An example of atmospheric Pb contamination around Port Pirie, South Australia, where a Pb smelter has operated since the 1890s, is shown to illustrate potential environmental applications of this new dataset. Other applications may include elucidating detail of Australian crustal evolution and mineralisation-related investigations.&nbsp;</div> <b>Citation:</b> Desem, C. U., de Caritat, P., Woodhead, J., Maas, R., and Carr, G.: A regolith lead isoscape of Australia, <o>Earth Syst. Sci. Data</i>, 16, 1383–1393, https://doi.org/10.5194/essd-16-1383-2024, 2024.

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.

Australian iron ore is predominantly exported and used for steelmaking internationally. However, steelmaking is an energy- and carbon-intensive heavy industry, and its electrification in the coming decades will likely disrupt the existing iron ore–steel value chains. Green steel—produced using hydrogen and electricity from renewable energy sources—presents both opportunities and challenges for Australia. Indeed, with abundant renewable energy potential and iron-ore resources, Australia could lead this global transformation. Here, we examine the interrelationships between the Australian iron-ore industry, the production of green-hydrogen from renewable energy sources, and an emergent green steelmaking process. In particular, we undertake detailed case studies to estimate current green steel production costs within two regions; the Pilbara Craton in Western Australia and the Eyre Peninsula in South Australia. While existing technology is not well suited to Australian hematite ores, our analysis highlights the site-specific competitiveness of small-scale, magnetite-fed, off-grid operations. The results underscore the advantages of a well-optimised system in decreasing hydrogen and energy storage requirements, and decreasing production costs. While our results also suggest that grid-connected projects could reduce costs through flexible operation, more work is required to understand the limitations of these conclusions. The results underscore the need to develop technologies to utilise hematite ores in green steelmaking, but also highlight the opportunity for this emerging industry to commercialise Australia’s magnetite resources. <b>Citation: </b>Wang C., Walsh S. D. C., Haynes M. W., Weng Z., Feitz A., Summerfield D., & Lutalo I., 2022. From Australian iron ore to green steel: the opportunity for technology-driven decarbonisation. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/147005