The National Geochemical Survey of Australia (NGSA) is Australia’s first national-scale geochemical survey. It was delivered to the public on 30 June 2011, after almost five years of stakeholder engagement, strategic planning, sample collection, preparation and analysis, quality assurance/quality control, and preliminary data analytics. The project was comprehensively documented in seven initial open-file reports and six data and map sets, followed over the next decade by more than 70 well-cited scientific publications. This review compiles the body of work and knowledge that emanated from the project to-date as an indication of the impact the NGSA had over the decade 2011-2021. The geochemical fabric of Australia as never seen before has been revealed by the NGSA. This has spurred further research and stimulated the mineral exploration industry. This paper also critically looks at operational decisions taken at project time (2007-2011) that were good and perhaps – with the benefit of hindsight – not so good, with the intention of providing experiential advice for any future large-scale geochemical survey of Australia or elsewhere. Strengths of the NGSA included stakeholder engagement, holistic approach to a national survey, involvement of other geoscience agencies, collaboration on quality assurance with international partners, and targeted promotion of results. Weaknesses included gaining successful access to all parts of the nation, and management of sample processing in laboratories. <b>Citation:</b> Patrice de Caritat; The National Geochemical Survey of Australia: review and impact. <i>Geochemistry: Exploration, Environment, Analysis </i>2022;; 22 (4): geochem2022–032. doi: https://doi.org/10.1144/geochem2022-032 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 – 2018). The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8’s OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest - Dale Roberts, John Wilford and Omar Ghattas Ghattas (2019). Nature Communications, DOI: 10.1038/s41467-019-13276-1. https://www.nature.com/articles/s41467-019-13276-1

During the last 10-20 years, Geological Surveys around the world have undertaken a major effort towards delivering fully harmonized and tightly quality controlled low-density multi-element soil geochemical maps and datasets of vast regions including up to whole continents. Concentrations of between 45 and 60 elements commonly have been determined in a variety of different regolith types (e.g., sediment, soil). The multi-element datasets are published as complete geochemical atlases and made available to the general public. Several other geochemical datasets covering smaller areas but generally at a higher spatial density are also available. These datasets may, however, not be found by superficial internet-based searches because the elements are not mentioned individually either in the title or in the keyword lists of the original references. This publication attempts to increase the visibility and discoverability of these fundamental background datasets covering large areas up to whole continents. <b>Citation:</b> P. de Caritat, C. Reimann, D.B. Smith, X. Wang, Chemical elements in the environment: Multi-element geochemical datasets from continental- to national-scale surveys on four continents, <i>Applied Geochemistry</i>, Volume 89, 2018, Pages 150-159, ISSN 0883-2927, https://doi.org/10.1016/j.apgeochem.2017.11.010

A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted.

A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.

Plutonium (Pu) interactions in the environment are highly complex. Site-specific variables play an integral role in determining the chemical and physical form of Pu, and its migration, bioavailability, and immobility. This paper aims to identify the key variables that can be used to highlight regions of radioecological sensitivity and guide remediation strategies in Australia. Plutonium is present in the Australian environment as a result of global fallout and the British nuclear testing program of 1952 – 1958 in central and west Australia (Maralinga and Monte Bello islands). We report the first systematic measurements of 239+240Pu and 238Pu activity concentrations in distal (≥1,000 km from test sites) catchment outlet sediments from Queensland, Australia. The average 239+240Pu activity concentration was 0.29 mBq.g -1 (n = 73 samples) with a maximum of 4.88 mBq.g -1. 238Pu/239+240Pu isotope ratios identified a large range (0.02 – 0.29 (RSD: 74%)) which is congruent with the heterogeneous nuclear material used for the British nuclear testing programme at Maralinga and Montebello Islands. The use of a modified PCA relying on non-linear distance correlation (dCorr) provided broader insight into the impact of environmental variables on the transport and migration of Pu in this soil system. Primary key environmental indicators of Pu presence were determined to be actinide/lanthanide/heavier transition metals, elevation, electrical conductivity (EC), CaO, SiO2, SO3, landform, geomorphology, land use, and climate explaining 81.7% of the variance of the system. Overall this highlighted that trace level Pu accumulations are associated with the coarse, refractive components of Australian soils, and are more likely regulated by the climate of the region and overall soil type. <b>Citation:</b> Megan Cook, Patrice de Caritat, Ross Kleinschmidt, Joёl Brugger, Vanessa NL. Wong, Future migration: Key environmental indicators of Pu accumulation in terrestrial sediments of Queensland, Australia,<i> Journal of Environmental Radioactivity</i>, Volumes 223–224, 2020, 106398,ISSN 0265-931X, https://doi.org/10.1016/j.jenvrad.2020.106398

<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>With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates to predict and map Li content across the 7.6 million km2 area of Australia. Soil samples were collected by the National Geochemical Survey of Australia at a total of 1315 sites, with both top (0–10 cm depth) and bottom (on average 60–80 cm depth) catchment outlet sediments sampled. We developed 50 bootstrap models using a Cubist regression tree algorithm for both depths. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction with an RMSE of 3.82 mg kg-1 for the top depth. The model for the bottom depth has yet to be validated. The variables of importance for the models indicated that the first three Landsat bands and gamma radiometric dose have a strong impact on Li prediction. The bootstrapped models were then used to generate digital soil Li prediction maps for both depths, which could select and delineate areas with anomalously high Li concentrations in the regolith. The map shows high Li concentration around existing mines and other potentially anomalous Li areas. The same mapping principles can potentially be applied to other elements.&nbsp;</div> <b>Citation:</b> Ng, W., Minasny, B., McBratney, A., de Caritat, P., and Wilford, J.: Digital soil mapping of lithium in Australia, <i>Earth Syst. Sci. Data</i>, 15, 2465–2482, https://doi.org/10.5194/essd-15-2465-2023, <b>2023</b>.

<div>Strontium isotopes (87Sr/86Sr) are useful in the earth sciences (e.g. recognising geological provinces, studying geological processes) as well in archaeological (e.g. informing on past human migrations), palaeontological/ecological (e.g. investigating extinct and extant taxa’s dietary range and migrations) and forensic (e.g. validating the origin of drinks and foodstuffs) sciences. Recently, Geoscience Australia and the University of Wollongong have teamed up to determine 87Sr/86Sr ratios in fluvial sediments selected mostly from the low-density National Geochemical Survey of Australia (www.ga.gov.au/ngsa), with a few additional Northern Australia Geochemical Survey infill samples. The present study targeted the northern parts of Western Australia, the Northern Territory and Queensland in Australia, north of 21.5 °S. The samples were taken mostly from a depth of ~60-80 cm depth in floodplain deposits at or near the outlet of large catchments (drainage basins). A coarse grain-size fraction (&lt;2 mm) was air-dried, sieved, milled then digested (hydrofluoric acid + nitric acid followed by aqua regia) to release total strontium. Preliminary results demonstrate a wide range of strontium isotopic values (0.7048 &lt; 87Sr/86Sr &lt; 1.0330) over the survey area, reflecting a large diversity of source rock lithologies, geological processes and bedrock ages. Spatial distribution of 87Sr/86Sr shows coherent (multi-point anomalies and smooth gradients), large-scale (&gt;100 km) patterns that appears to be consistent, in many places, with surface geology, regolith/soil type and/or nearby outcropping bedrock. For instance, the extensive black clay soils of the Barkly Tableland define a &gt;500 km-long northwest-southeast-trending low anomaly (87Sr/86Sr &lt; 0.7182). Where carbonate or mafic igneous rocks dominate, a low to moderate strontium isotope signature is observed. In proximity to the outcropping Proterozoic metamorphic provinces of the Tennant, McArthur, Murphy and Mount Isa geological regions, conversely, high 87Sr/86Sr values (&gt; 0.7655) are observed. A potential link between mineralisation and elevated 87Sr/86Sr values in these regions needs to be investigated in greater detail. Our results to-date indicate that incorporating soil/regolith strontium isotopes in regional, exploratory geoscience investigations can help identify basement rock types under (shallow) cover, constrain surface processes (e.g. weathering, dispersion), and, potentially, recognise components of mineral systems. Furthermore, the resulting strontium isoscape and model derived therefrom can also be utilised in archaeological, paleontological and ecological studies that aim to investigate past and modern animal (including humans) dietary habits and migrations. &nbsp;The new spatial dataset is publicly available through the Geoscience Australia portal https://portal.ga.gov.au/.</div>

<div>Exploring for the Future (2016-2024) was an Australian Government program led by Geoscience Australia (GA), in partnership with state and Northern Territory governments. The program aim was to drive industry investment in resource exploration in frontier regions of Australia by providing new precompetitive data and information about energy, mineral and groundwater resource potential. To address this overarching objective, GA led a key element of the Australian Government’s commitment to achieve net zero by 2050. The energy transition to an ever decreasing carbon emission economy will involve the increasing use of hydrogen gas (hydrogen). The key benefit of using hydrogen is that it is a clean fuel, emitting only water vapour and heat when combusted. However, hydrogen today is manufactured at a relatively high cost. The recent discovery of a 98% per cent pure natural hydrogen field in Mali (Africa) has led to low cost hydrogen production. It has also captured the imagination of explorers and the search is now on for new natural hydrogen accumulations across the world. Australia is considered one of the most prospective locations for sub-surface natural hydrogen due to our ancient geology and potential presence of suitable hydrogen traps. A review of occurrences of hydrogen in natural sub-surface rocks found high concentrations of hydrogen in central western, New South Wales (NSW). Helium is extracted in commercial quantities from natural gas and Australia currently has no local production. This project, in collaboration with the Geological Survey of NSW (GSNSW), built on the desktop review and has identified new occurrences of natural hydrogen and helium through soil gas surveys in various locations across central and far west, NSW. To support the Exploring for the Future program, six soil gas surveys for natural hydrogen and helium were jointly undertaken by staff from GA and GSNSW across central and western NSW during 2022-23. The project also included sites near the Tumut township to test various soil gas sampling techniques as well as the major focus in the Curnamona Province and Delamerian Orogen in far west New South Wales. In the first phase of the project, conceptual geological models for natural hydrogen and helium generation and accumulation were developed using pre-existing geoscientific data, including electromagnetic, magnetotelluric, magnetic, gravity, radiometric, drilling, seismic and satellite-derived data. The selected sites represented various concepts for natural hydrogen and helium generation, such as granite rocks rich in potassium, thorium, and uranium, banded iron formations, ultramafic rocks, and diatremes. The second phase of the project was the collection of soil gases from shallow (1 m deep) installations and subsequent molecular and isotopic compositional analysis at the GA Laboratory. Maximum hydrogen and helium concentrations in the soil gases are 309.5 ppm and 35.3 ppm, respectively, which is comparable to and even exceeds previously reported soil gas surveys both in Western Australia and overseas. The final phase was the integration of all datasets within a GIS platform for the interpretation and presentation of maps within this report.</div>