Managed aquifer recharge (MAR) enhances recharge to aquifers. As part of the Exploring for the Future Southern Stuart Corridor project, remotely sensed data were used to map regolith materials and landforms, and to identify areas that represent potential MAR target areas for future investigation. Nine areas were identified, predominantly associated with alluvial landforms in low-gradient landscape settings. The surface materials are typically sandy, or sandy and silty, with the prospective areas overlying newly identified groundwater resources associated with Paleozoic sedimentary rocks of the Wiso and Georgina basins. The workflow used here can be rapidly rolled out across broader areas, and can be supplemented by higher-resolution, longer time-series remote-sensing data, coupled with data analytics, modelling and expert knowledge. Such an approach will help to identify areas of the arid interior that may be suitable for MAR schemes that could supplement water for remote communities, and agricultural and other natural resource developments. <b>Citation:</b> Smith, M.L., Hostetler, S. and Northey, J., 2020. Managed aquifer recharge prospectivity mapping in the Northern Territory arid zone using remotely sensed data. 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.

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.

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.

Estimating the relative contributions of bedrock geology, mineralisation and anthropogenic contamination to the chemistry of samples collected at the Earth’s surface is critical in research and application fields as diverse as environmental impact studies and regional mineral exploration programs. The element lead (Pb) is a particularly useful tracer in this context, representing a toxin of environmental concern and associated with many other anthropogenic contaminants (e.g. mine wastes, waters, paints, aerosols), as well as with mineralisation. Although Pb concentration data are frequently collected in geochemical studies, isotopic analysis offers an important advantage, allowing discrimination between different sources of Pb. The Pb isotopic composition of regolith is likely to reflect contributions from underlying rock (including Pb-rich mineralisation), wind-blown dust and possibly anthropogenic sources (industry, transport, agriculture, residential, waste handling). Regolith samples collected at different depths may show distinct compositions; bedrock isotopic signatures are expected to dominate in deeper soils, whilst airborne dust and anthropogenic signatures are more important at the surface. Pb isotope ratios in the continental crust show large variations, which will be transferred to the regolith, providing a potentially unique bedrock signal that is easily measured. This research program examines if soil Pb isotope mapping can identify the underlying geology and metallogenic provinces, if different sampling and analytical approaches produce very different results, and how anthropogenic signals vary across the continent. Here, we present our results for the Northern Territory, where single regolith samples from many (not all) catchments define apparently consistent isotopic domains that can be interpreted in relation to the underlying geology (crystalline basement, basins) and mineral deposits. <b>Citation:</b> Desem, C.U., Maas, R., Woodhead, J., Carr, G. and de Caritat P., 2020. Towards a Pb isotope regolith map of the Australian continent: a Northern Territory perspective. 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.

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 (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

<div>Geochemistry of soils, stream sediments, and overbank sediments, plays an important part in informing geochemical environmental baselines, mineral prospectivity, and environmental management practices. Australia has a large number of such surveys, but they are spatially isolated and often used in isolation. First released in 2020, the Levelled Geochemical Baseline of Australia focused on levelling such surveys across the North Australian Craton, so that they could be used as a seamless dataset. This data release acts as an update to the Levelled Geochemical Baseline of Australia by changing the focus to national scale and incorporating recently reanalysed legacy samples.</div><div><br></div><div>This work was undertaken as part of the Exploring for the Future program, an eight-year program by the Australian government. The 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, was an eight year, $225m investment by the Australian Government.</div><div><br></div><div><br></div><div><br></div><div><br></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.