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

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.

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)

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.

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

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.

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.

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.

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.

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