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 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

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

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

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

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.

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 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.