The regolith landform maps are drawn at various scales and illustrate the distribution of regolith materials and the landforms on which they occur. Regolith landforms are described using the regolith terrain mapping (RTMAP) scheme developed at Geoscience Australia or the Residual-Erosional-Depositional (RED) mapping scheme developed by the CSIRO Division of Exploration and Mining.

Australia has a significant number of surface sediment geochemical surveys that have been undertaken by industry and government over the past 50 years. These surveys represent a vast investment and have up to now only been able to be used in isolation, independently from one another. The key to maximising the full potential of these data and the information they provide for mineral exploration, environmental management and agricultural purposes is using all the surveys together, seamlessly. These disparate geochemical surveys not only sampled various landscape elements and analysed a range of size fractions, but also used multiple analytical techniques, instrument types and laboratories. The geochemical data from these surveys require levelling to eliminate, as much as possible, non-geological variation. Using a variety of methodologies, including reanalysis of both international standards and small subsets of samples from previous surveys, we have created a seamless surface geochemical map for northern Australia, from nine surveys with 15,605 samples. We tested our approach using two surveys from the southern Thomson Orogen, which demonstrated the successful removal of inter-laboratory and other analytical variation. Creation of the new combined and levelled northern Australian dataset paves the way for the application of statistical and data analytics techniques, such as principal component analysis and machine learning, thereby maximising the value of these legacy data holdings. The methodology documented here can be applied to additional geochemical datasets as they become available.

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.

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 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.

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)

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.

The Regolith Map of Australia 1:5M scale dataset (2013 edition) is a seamless but partial national coverage of regolith-landform units, compiled for use at, or between 1:5 million, and 1:1 million scale. The data maps high-level regolith-landform units. The units appear as polygon geometries, and with attribute information identifying high-level regolith and landform nomenclatures and their hierarchy. The 2013 dataset is a completely new portrayal of Australia's regolith from that presented much earlier in 1986, in which a whole of continent view of Australia's regolith was based on a simpler desktop-based 1:5 million continental regolith terrain assessment, not directly linked with landforms and published by the Bureau of Mineral Resources Geology and Geophysics. The 2013 edition incorporates new published mapping in South Australia (2012), integrated with earlier field-based regolith-landform mapping data from the Northern Territory (2006) and later Queensland (2008). The attribute structure of the new dataset is also revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information. The map data is compiled largely from simplifying and edge-matching of 1:250 000 scale regolith compilation maps. Some source regolith and geologic maps ranging in scale from 1:50 000 to 1:1 million were used together with LANDSAT7, radiometric, magnetics, and gravity imagery, in addition to a 9 second digital elevation model.

The AEM method measures regolith and rocks' bulk subsurface electrical conductivity, typically to a depth of several hundred meters. AEM survey data is widely used in Australia for mineral exploration (i.e. mapping undercover and detection of mineralisation), groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). Geoscience Australia (GA) has flown Large regional AEM surveys over Northern Australia, including Queensland, Northern Territory and Western Australia. The surveys were flown nominally at 20-kilometre line spacing, using the airborne electromagnetic systems that have signed technical deeds of staging with GA to ensure they can be modelled quantitatively. Geoscience Australia commissioned the survey as part of the Exploring for the Future (EFTF) program. The EFTF program is led by Geoscience Australia (GA), in collaboration with the Geological Surveys of the Northern Territory, Queensland, South Australia and Western Australia, and is investigating the potential mineral, energy and groundwater resources in northern Australia and South Australia. We have used a machine learning modelling approach that establishes predictive relationships between the inverted flight-line modelled conductivity with a suite of national environmental and geological covariates. These covariates include terrain derivatives, gamma-ray radiometric, geological maps, climate derived surfaces and satellite imagery. Conductivity-depth values were derived from a single model using GA's deterministic 1D smooth-30-layer layered-earth-inversion algorithm. (Brodie and Richardson 2015). Three conductivity depth interval predictions are generated to interpolate the actual modelled conductivity data, which is 20km apart. These depth slices include a 0-50cm, 9-11m and 22-27m depth prediction. Each depth interval was modelled and individually optimised using the gradient boosted tree algorithm. The training cross-validation step used label clusters or groups to minimise over-fitting. Many hundreds of conductivity models are generated (i.e. ensemble modelling). Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th) to measure model uncertainty. Grids show conductivity (S/m) in log 10 units. Reported out-of-sample r-squares for each interval in order of increasing depth are 0.74, 0.64, and 0.67. A decline in model performance with increasing depth was expected due to the decrease in suitable covariates at greater depths. Modelled conductivities seem to be consistent with the geological, regolith, geomorphological, and climate processes in the study area. The conductivity grids are at the resolution of the covariates, which have a nominal pixel size of 85 meters. Datasets in this data package include; 1. 0-50cm depth interval 0_50cm_median.tif; 0_50_upper.tif; 0_50_lower.tif 2. 9-11m depth interval 9_11m_median.tif; 9_11m_upper.tif; 9_11m_lower.tif 3. 22-27m depth interval 22_27_median.tif; 22_27_upper.tif; 22_27_lower.tif 4. Covariate shift; Cov_shift.tif (higher values = great shift in covariates) Reference: Ross C Brodie & Murray Richardson (2015) Open Source Software for 1D Airborne Electromagnetic Inversion, ASEG Extended Abstracts, 2015:1, 1-3, DOI: 10.1071/ ASEG2015ab197

A seamless Regolith Map of Australia drawn from field-defined regolith-landform data at approx 1:250k scale for QLD and NT, and additionally from SA regolith data derived from the South Australian Regolith Map (1:2 Million) published in 2012, and generalised by Geoscience Australia to 1:5 000 000 for matching with existing data.