<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>

Weathering intensity or the degree of weathering 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. 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.

Weathering is an important process of the Earth’s surface that has a major influence on the chemical and physical properties of rock and soil. The intensity of this process largely controls the degree to which primary minerals are altered to secondary components, including clay and oxide minerals. The degree of surface weathering is particularly important in Australia, where variations in weathering intensity correspond to differences in the nature and distribution of regolith (weathered bedrock and sediments), which mantles approximately 80% of the Australian continent. Here, I use a random forest decision tree machine learning algorithm to first establish a relationship between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. I then apply this relationship to generate an improved national model of surface to near-surface weathering intensity. Covariates include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The model performs very well, with an r-squared correlation of 0.85 based on 5 K-fold cross-validation on the mean and standard deviation of 300 random forest models. This new weathering intensity model has broad utility for mineral exploration in variably weathered landscapes, agricultural mapping of chemical and physical soil attributes, ecology, and advancing the understanding of weathering processes within the upper regolith. <b>Citation:</b> Wilford, J., 2020. Revised weathering intensity model of Australia. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

Satellite imagery provides useful data for mapping the characteristics of exposed rock and soil. However, these materials in many environments are masked by vegetation. To address this the Sentinel-2 Barest Earth thematic product provides a national scale mosaic of the Australian continent with significantly reduced influence of seasonal vegetation cover to support enhanced mapping of soil and geology. The barest earth algorithm, operating on all available Sentinel-2 A and Sentinel-2 B observations up to September 2020, preferentially weights bare ground exposure through time to more directly map the surface mineralogy and geochemistry of soil and rock. The algorithm uses a high-dimensional weighted geometric median approach that maintains the spectral relationships across all Sentinel-2 bands building on a similar approach applied to the deeper Landsat time series archive. Both barest earth products have spectral bands in the visible near infrared and shortwave infrared region of the electromagnetic spectrum. However, the main visible and near-infrared Sentinel-2 bands have a spatial resolution of 10 meters compared to 30m for the Landsat TM equivalents. Furthermore, although the first Sentinel-2 satellite was launched in 2015 the twin orbiting satellite configuration provides shorter revisit times that increase the frequency of observations and probability of observing barer pixels amongst observations obscured by clouds and shadows. The Sentinel-2 satellite has broad application in environmental and geological sciences. The barest earth approach generates a complimentary set of spectral bands with reduced vegetation influence for more directed geological applications. We discuss the barest earth algorithm and compare non-bare and barest earth Sentinel-2 imagery. A series of enhance Sentinel-2 images are used to illustrate the potential of the barest earth datasets for mapping soil and bedrock and we summarise key band ratios that can be used as proxies for mapping surface mineralogy including iron oxides and hydroxyl minerals.

The results of a pilot study into the application of an unsupervised clustering approach to the analysis of catchment-based National Geochemical Survey of Australia (NGSA) geochemical data combined with geophysical and geological data across northern Australia are documented. NGSA Mobile Metal Ion® (MMI) element concentrations and first and second order statistical summaries across catchments of geophysical data and geological data are integrated and analysed using Self-Organising Maps (SOM). Input features that contribute significantly to the separation of catchment clusters are objectively identified and assessed. A case study of the application of SOM for assessing the spatial relationships between Au mines and mineral occurrences in catchment clusters is presented. Catchments with high mean Au code-vector concentrations are found downstream of areas known to host Au mineralisation. This knowledge is used to identify upstream catchments exhibiting geophysical and geological features that indicate likely Au mineralisation. The approach documented here suggests that catchment-based geochemical data and summaries of geophysical and geological data can be combined to highlight areas that potentially host previously unrecognised Au mineralisation. <b>Citation:</b> M. J. Cracknell, P. de Caritat; Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia. <i>Geochemistry: Exploration, Environment</i>, Analysis 2017; 17 (3): 204–216. doi: https://doi.org/10.1144/geochem2016-012 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

Remotely sensed data and updated DEM and radiometric datasets, combined with existing surface material and landform mapping were used to map regolith landform units for the Ti Tree, Western Davenport and Tennant Creek regions of the SSC project. This report describes the methods used and outlines the new mapping.

The National Geochemical Survey of Australia (NGSA) provides an internally consistent, state-of-the-art, continental-scale geochemical dataset that can be used to assess areas of Australia more elevated in commodity metals and/or pathfinder elements than others. But do regions elevated in such elements correspond to known mineralized provinces, and what is the best method for detecting and thus potentially predicting those? Here, using base metal associations as an example, I compare a trivariate rank-based index and a multivariate-based Principal Component Analysis method. The analysis suggests that the simpler rank-based index better discriminates catchments endowed with known base metal mineralization from barren ones and could be used as a first-pass prospectivity tool. <b>Citation:</b> Patrice de Caritat, Continental-scale geochemical surveys and mineral prospectivity: Comparison of a trivariate and a multivariate approach, <i>Journal of Geochemical Exploration</i>, Volume 188, 2018, Pages 87-94, ISSN 0375-6742, https://doi.org/10.1016/j.gexplo.2018.01.014

Mapped and projected extents of geology and geologic features in Australia, including: surface geology, regolith geology, solid geology, chronostratigraphic surfaces, and province boundaries. The database includes igneous, sedimentary and structural characteristics, age limits, parent and constituent units, relations to surrounding provinces, and mineral and petroleum resources. based on field observations interpretations of geophysics and borehole data. <b>Value:</b> Data used for understanding surface and near surface geology. The data can be used for a variety of purposes, including resource exploration, land use management, and environmental assessment. <b>Scope:</b> Australia and Australian Antarctic Territory

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/).

The National Geochemical Survey of Australia (<a href="http://www.ga.gov.au/ngsa" title="NGSA website" target="_blank">NGSA</a>) is Australia’s only internally consistent, continental-scale <a href="http://dx.doi.org/10.11636/Record.2011.020" title="NGSA geochemical atlas and dataset" target="_blank">geochemical atlas and dataset</a>. The present dataset contains additional mineralogical data obtained on NGSA samples selected from the Darling-Curnamona-Delamerian (<a href="https://www.ga.gov.au/eftf/projects/darling-curnamona-delamerian" title="DCD website" target="_blank">DCD</a>) region of southeastern Australia for the first partial data release of the Heavy Mineral Map of Australia (HMMA) project. The HMMA, a collaborative project between Geoscience Australia and Curtin University underpinned by a pilot project establishing its feasibility, is part of the Australian Government-funded Exploring for the Future (<a href="https://www.ga.gov.au/eftf" title="EFTF website" target="_blank">EFTF</a>) program. The selected 223 NGSA sediment samples fall within the DCD polygon plus an approximately one-degree buffer. The samples were taken on average from 60 to 80 cm depth in floodplain landforms, dried and sieved to a 75-430 µm grainsize fraction, and the contained heavy minerals (HMs; i.e., those with a specific gravity >2.9 g/cm³) were separated by dense fluids and mounted on cylindrical epoxy mounts. After polishing and carbon-coating, the mounts were subjected to automated mineralogical analysis on a TESCAN® Integrated Mineral Analyzer (TIMA). Using scanning electron microscopy and backscatter electron imaging integrated with energy dispersive X-ray analysis, the TIMA identified over 140 different HMs in the DCD area. The dataset, consisting of over 29 million individual mineral grains identified, was quality controlled and validated by an expert team. The data released here can be visualised, explored and downloaded using an online, bespoke mineral network analysis tool (<a href="https://geoscienceaustralia.shinyapps.io/mna4hm/" title="MNA website" target="_blank">MNA</a>) built on a cloud-based platform. Accompanying this report are a data file of TIMA results and a mineralogy vocabulary file. When completed in 2023, it is hoped the HMMA project will positively impact mineral exploration and prospectivity modelling around Australia, as well as have other applications in earth and environmental sciences.