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.

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)

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

Bulk quantitative mineralogy of regolith is a useful indicator of lithological precursor (protolith), degree of weathering, and soil properties affecting various potential landuse decisions. To date, no national-scale maps of regolith mineralogy are available in Australia. Catchment outlet sediments collected over 80% of the continent as part of the National Geochemical Survey of Australia (NGSA) afford a unique opportunity to rapidly and cost-effectively determine regolith mineralogy using the archived sample material. This report releases mineralogical data and metadata obtained as part of a feasibility study in a selected pilot area for such a national regolith mineralogy database and atlas. The area chosen for this study is within the Darling-Curnamona-Delamerian (DCD) region of southeastern Australia. The DCD region was selected as a ‘deep-dive’ data acquisition and analysis by the Exploration for the Future (2020-2024) federal government initiative managed at Geoscience Australia. One hundred NGSA sites from the DCD region were prepared for X-Ray Diffraction (XRD) analysis, which consisted of qualitative mineral identification of the bulk samples (i.e., ‘major’ minerals), qualitative clay mineral identification of the <2 µm grain-size fraction, and quantitative analysis of both ‘major’ and clay minerals of the bulk sample. The identified mineral phases were quartz, plagioclase, K-feldspar, calcite, dolomite, gypsum, halite, hematite, goethite, rutile, zeolite, amphibole, talc, kaolinite, illite (including muscovite and biotite), palygorskite (including interstratified illite-smectite and vermiculite), smectite (including interstratified illite-smectite), vermiculite, and chlorite. Poorly diffracting material (PDM) was also quantified and reported as ‘amorphous’. Mineral identification relied on the EVA® software, whilst quantification was performed using Siroquant®. Resulting mineral abundances are reported with a Chi-squared goodness-of-fit between the actual diffractogram and a modelled diffractogram for each sample, as well as an estimated standard error (esd) measurement of uncertainty for each mineral phase quantified. Sensitivity down to 0.1 wt% (weight percent) was achieved, with any mineral detection below that threshold reported as ‘trace’. Although detailed interpretation of the mineralogical data is outside the remit of the present data release, preliminary observations of mineral abundance patterns suggest a strong link to geology, including proximity to fresh bedrock, weathering during sediment transport, and robust relationships between mineralogy and geochemistry. The mineralogical data generated by this study are presented in Appendix A of this report and are downloadable as a .csv file. Mineral abundance or presence/absence maps are shown in Appendices B and C to document regional mineralogical patterns.

<div>Bulk quantitative mineralogy of regolith is a useful indicator of lithological precursor (protolith), degree of weathering, and soil properties affecting various potential landuse decisions. To date, no empirical national-scale maps of regolith mineralogy are available in Australia. Satellite-derived mineralogical proxy products exist, however, they require on-the-ground validation. Catchment outlet sediments collected over 80% of the continent as part of the National Geochemical Survey of Australia (NGSA) afford a unique opportunity to rapidly and cost-effectively determine regolith mineralogy using the archived sample material. This report releases mineralogical data and metadata obtained as part of a study extending a previous pilot project for such a national regolith mineralogy database and atlas.</div><div>The area chosen for this study includes the part of South Australia not inside the pilot project, which focussed on the 2020-2024 Exploring for the Future (EFTF) Darling-Curnamona-Delamerian (DCD) region of southeastern Australia, as well as the EFTF Barkly-Isa-Georgetown (BIG) region of northern Australia. The South Australian part of the study was selected because the Geological Survey of South Australia indicated interest in expanding the pilot (DCD) project to the rest of the State. The BIG region was selected because it is a ‘deep-dive’ data acquisition and analysis area within the EFTF Australian Government initiative managed at Geoscience Australia. The whole study area essentially describes a continuous north-south trans-continental transect spanning South Australia (SA), Queensland (Qld) and the Northern Territory (NT), and is herein abbreviated as SA-Qld-NT.</div><div>Two hundred and sixty four NGSA sites from the SA-Qld-NT region were prepared for X-Ray Diffraction (XRD) analysis, which consisted of qualitative mineral identification of the bulk samples (i.e., ‘major’ minerals), qualitative clay mineral identification of the <2 µm grain-size fraction, and quantitative analysis of both major and clay minerals of the bulk sample. The identified mineral phases were quartz, kaolinite, plagioclase, K-feldspar, nosean (a sulfate bearing feldspathoid), calcite, dolomite, aragonite, ankerite, hornblende, gypsum, bassanite (a partially hydrated calcium sulfate), halite, hematite, goethite, magnetite, rutile, anatase, pyrite, interstratified or mixed-layer illite-smectite, smectite, muscovite, chlorite (group), talc, palygorskite, jarosite, alunite, and zeolite (group). Poorly diffracting material was also quantified and reported as ‘amorphous.’ Mineral identification relied on the EVA® software, whilst quantification was performed using Siroquant®. Resulting mineral abundances are reported with a Chi-squared goodness-of-fit between the actual diffractogram and a modelled diffractogram for each sample, as well as an estimated standard error (esd) measurement of uncertainty for each mineral phase quantified. Sensitivity down to 0.1 weight percent (wt%) was achieved, with any mineral detection below that threshold reported as ‘trace.’ </div><div>Although detailed interpretation of the mineralogical data is outside the remit of the present data release, preliminary observations of mineral abundance patterns suggest a strong link to geology, including proximity to fresh bedrock, weathering during sediment transport, and robust relationships between mineralogy and geochemistry. The mineralogical data generated by this study are downloadable as a .csv file from the Geoscience Australia website (https://dx.doi.org/10.26186/147990).&nbsp;</div>

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.

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.

<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 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.

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