An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 – 2018). The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8’s OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest - Dale Roberts, John Wilford and Omar Ghattas Ghattas (2019). Nature Communications, DOI: 10.1038/s41467-019-13276-1. https://www.nature.com/articles/s41467-019-13276-1

A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted.

Data in the GEOCHEM database comprises inorganic geochemical analytical data and associated metadata. Geochemical data comprises concentration data (value, error, unit of measure) measured on a range of analytical instruments, for a range of elements of the periodic table. Associated metadata includes information on analytical techniques, analytical methodology, laboratory, analysts, date of analysis, detection limits, accuracy, and precision. The GEOCHEM database also records results for reference standards. Data is specifically for rocks, soils and other unconsolidated geological material and does not include oils, gases or water analyses. Geochemical data may be total rock (i.e., whole rock analysed) or for a variety of fractions of the total rock, e.g., various non-total acid digests, mineral separates, differing size fractions. It also includes quantitative to semi-quantitative data from field measurements, such as portable x-ray fluorescence (XRF). It does not include geochemical data for individual minerals. <b>Value: </b>Geochemical data underpins much geoscientific study, and is used directly to classify, characterise and understand geological material and its formation. It has direct relevance to understanding the formation of the earth, the continents, and the processes that create and shape the surface we live on. For example, this information is used within: both discovering and the understanding of mineral deposits we depend on; the nature, health and sustainability of the soils we live and farm on; as well as providing input into a range of potential geohazards. <b>Scope: </b>The collection includes data from over 60 years of Geoscience Australia (GA) and state/territory partner regional geological projects within Australia, as well as continental-scale and regional geochemical surveys like National Geochemical Survey of Australia (NGSA) and Northern Australia Geochemical Survey (NAGS) (Exploring for the Future- EFTF). It also includes data from other countries that GA has worked with, e.g., Papua New Guinea, Antarctica, Solomon Islands and New Zealand. Explore the <b>Geoscience Australia portal - <a href="https://portal.ga.gov.au/">https://portal.ga.gov.au/</a></b>

<div>Strontium isotopes (87Sr/86Sr) are useful in the earth sciences (e.g. recognising geological provinces, studying geological processes) as well in archaeological (e.g. informing on past human migrations), palaeontological/ecological (e.g. investigating extinct and extant taxa’s dietary range and migrations) and forensic (e.g. validating the origin of drinks and foodstuffs) sciences. Recently, Geoscience Australia and the University of Wollongong have teamed up to determine 87Sr/86Sr ratios in fluvial sediments selected mostly from the low-density National Geochemical Survey of Australia (www.ga.gov.au/ngsa), with a few additional Northern Australia Geochemical Survey infill samples. The present study targeted the northern parts of Western Australia, the Northern Territory and Queensland in Australia, north of 21.5 °S. The samples were taken mostly from a depth of ~60-80 cm depth in floodplain deposits at or near the outlet of large catchments (drainage basins). A coarse grain-size fraction (&lt;2 mm) was air-dried, sieved, milled then digested (hydrofluoric acid + nitric acid followed by aqua regia) to release total strontium. Preliminary results demonstrate a wide range of strontium isotopic values (0.7048 &lt; 87Sr/86Sr &lt; 1.0330) over the survey area, reflecting a large diversity of source rock lithologies, geological processes and bedrock ages. Spatial distribution of 87Sr/86Sr shows coherent (multi-point anomalies and smooth gradients), large-scale (&gt;100 km) patterns that appears to be consistent, in many places, with surface geology, regolith/soil type and/or nearby outcropping bedrock. For instance, the extensive black clay soils of the Barkly Tableland define a &gt;500 km-long northwest-southeast-trending low anomaly (87Sr/86Sr &lt; 0.7182). Where carbonate or mafic igneous rocks dominate, a low to moderate strontium isotope signature is observed. In proximity to the outcropping Proterozoic metamorphic provinces of the Tennant, McArthur, Murphy and Mount Isa geological regions, conversely, high 87Sr/86Sr values (&gt; 0.7655) are observed. A potential link between mineralisation and elevated 87Sr/86Sr values in these regions needs to be investigated in greater detail. Our results to-date indicate that incorporating soil/regolith strontium isotopes in regional, exploratory geoscience investigations can help identify basement rock types under (shallow) cover, constrain surface processes (e.g. weathering, dispersion), and, potentially, recognise components of mineral systems. Furthermore, the resulting strontium isoscape and model derived therefrom can also be utilised in archaeological, paleontological and ecological studies that aim to investigate past and modern animal (including humans) dietary habits and migrations. &nbsp;The new spatial dataset is publicly available through the Geoscience Australia portal https://portal.ga.gov.au/.</div>

<div>The ubiquitous nature of dust, along with localised chemical and biological signatures, makes it an ideal medium for provenance determination in a forensic context. Metabarcoding of dust can yield biological communities unique to the site of interest, similarly, geochemical and mineralogical analyses can uncover elements and minerals within dust than can be matched to a geographic location. Combining these analyses presents multiple lines of evidence as to the origin of collected dust samples. In this work, we investigated whether the time an item spent at a site collecting dust influenced the ability to assign provenance. We then integrated dust metabarcoding of bacterial and fungal communities into a framework amenable to forensic casework, (i.e., using calibrated log-likelihood ratios to predict the origin of dust samples) and assessed whether current soil metabarcoding databases could be utilised to predict dust origin. Furthermore, we tested whether both metabarcoding and geochemical/mineralogical analyses could be conducted on a single sample for situations where sampling is limited. We found both analyses could generate results capable of separating sites from a single swabbed sample and that the duration of time to accumulate dust did not impact site separation. We did find significant variation within sites at different sampling time periods, showing that bacterial and fungal community profiles vary over time and space – but not to the extent that they are non-discriminatory. We successfully modelled soil and dust samples for both bacterial and fungal diversity, developing calibrated log-likelihood ratio plots and used these to predict provenance for dust samples. We found that the temporal variation in community composition influenced our ability to predict dust provenance and recommend reference samples be collected as close to the sampling time as possible. Thus, our framework showed soil metabarcoding databases are capable of being used to predict dust provenance but the temporal variation in metabarcoded communities will need to be addressed to improve provenance estimates.&nbsp;</div> <b>Citation:</b> Nicole R. Foster, Duncan Taylor, Jurian Hoogewerff, Michael G. Aberle, Patrice de Caritat, Paul Roffey, Robert Edwards, Arif Malik, Michelle Waycott and Jennifer M. Young, The secret hidden in dust: Uncovering the potential to use biological and chemical properties of the airborne soil fraction to assign provenance and integrating this into forensic casework, <i>Forensic Science International: Genetics,</i> (2023) doi:https://doi.org/10.1016/j.fsigen.2023.102931

<div>The soil gas database table contains publicly available results from Geoscience Australia's organic geochemistry (ORGCHEM) schema and supporting oracle databases for gas analyses undertaken by Geoscience Australia's laboratory on soil samples taken from shallow (down to 1 m below the surface) percussion holes. Data includes the percussion hole field site location, sample depth, analytical methods and other relevant metadata, as well as the molecular and isotopic compositions of the soil gas with air included in the reported results. Acquisition of the molecular compounds are by gas chromatography (GC) and the isotopic ratios by gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). The concentrations of argon (Ar), carbon dioxide (CO₂), nitrogen (N₂) and oxygen (O₂) are given in mole percent (mol%). The concentrations of carbon monoxide (CO), helium (He), hydrogen (H₂) and methane (C₁, CH₄) are given in parts per million (ppm). Compound concentrations that are below detection limit (BDL) are reported as the value -99999. The stable carbon (¹³C/¹²C) and nitrogen (¹⁵N/¹⁴N) isotopic ratios are presented in parts per mil (‰) and in delta notation as δ¹³C and δ¹⁵N, respectively.</div><div><br></div><div>Determining the individual sources and migration pathways of the components of natural gases found in the near surface are useful in basin analysis with derived information being used to support exploration for energy resources (petroleum and hydrogen) and helium in Australian provinces. These data are collated from Geoscience Australia records with the results being delivered in the Soil Gas web services on the Geoscience Australia Data Discovery portal at https://portal.ga.gov.au which will be periodically updated.</div>

Soil is a ubiquitous material at the Earth's surface with potential to be a useful evidence class in forensic and intelligence applications. Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements) are obtained from the survey's 268 topsoil samples (0–5 cm depth; 1 sample per km2). The simultaneous provenancing approach is underpinned by (i) the calculation of Spearman's correlation coefficients (rS) between an evidentiary sample and all the samples in the database for all variables generated by each analytical method; and (ii) the preparation of an interpolated raster grid of rS for each evidentiary sample and method resulting in a series of provenance rasters (“heat maps”). The simultaneous provenancing method is tested on the North Canberra soil survey with three “blind” samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR (mineralogy) and XRF (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications. <b>Citation:</b> de Caritat, P, Woods, B, Simpson, T, Nichols, C, Hoogenboom, L, Ilheo, A, et al. Forensic soil provenancing in an urban/suburban setting: A simultaneous multivariate approach. <i>J Forensic Sci</i>. 2022; 67: 927–935. https://doi.org/10.1111/1556-4029.14967

<div>Soil is a complex and spatially variable material that has a demonstrated potential to be a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain searches and prioritise resources by triaging areas as low and high interest is advantageous. Conducted between 2017 and 2021, a forensically relevant topsoil survey (0-5 cm depth; 1 sample per 1 km2) has been carried out over Canberra, Australia, with the aims of documenting the distribution of chemical elements in an urban/suburban environment, and of acting as a testbed for investigating various aspects of forensic soil provenancing. Geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for trace elements) following a total digestion (HF + HNO3) were obtained from the survey’s 685 topsoil samples (plus 138 additional quality control samples and six “Blind” simulated evidentiary samples). Using those “Blind” samples, we document a likelihood ratio approach where for each grid cell the analytical similarity between the grid cell and evidentiary sample is attributed from a measure of overlap between both Cauchy distributions, including appropriate uncertainties. Unlike existing methods that base inclusion/exclusion on an arbitrary threshold (e.g., ± three standard deviations), our approach is free from strict binary or Boolean thresholds, providing an unconstrained gradual transition dictated by the analytical similarity. Using this provenancing model, we present and evaluate a new method for upscaling from a fine (25 m x 25 m) interpolated grid to a more appropriate coarser (500 m x 500 m) grid, in addition to an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material. Our results show this collective procedure generates more consistent and robust provenance maps between two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), grid placements (e.g., grid shifts to the north or east) and theoretical users (e.g., different computer systems, or forensic geoscientists).</div> <b>Citation:</b> Michael G. Aberle, Patrice de Caritat, James Robertson, Jurian A. Hoogewerff, A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach,<i> Forensic Science International</i>, Volume 353, 2023, 111883, ISSN 0379-0738. https://doi.org/10.1016/j.forsciint.2023.111883.

<b>Please Note:</b> The data related to this Abstract can be obtained by contacting <a href = "mailto: clientservices@ga.gov.au">Manager Client Services</a> and quoting Catalogue number 144231. The data are arranged by regions, so please download the Data Description document found in the Downloads tab to determine your area of interest. Remotely sensed datasets provide fundamental information for understanding the chemical, physical and temporal dynamics of the atmosphere, lithosphere, biosphere and hydrosphere. Satellite remote sensing has been used extensively in mapping the nature and characteristics of the terrestrial land surface, including vegetation, rock, soil and landforms, across global to local-district scales. With the exception of hyper-arid regions, mapping rock and soil from space has been problematic because of vegetation that either masks the underlying substrate or confuses the spectral signatures of geological materials (i.e. diagnostic mineral spectral features), making them difficult to resolve. As part of the Exploring for the Future program, a new barest earth Landsat mosaic of the Australian continent using time-series analysis significantly reduces the influence of vegetation and enhances mapping of soil and exposed rock from space. Here, we provide a brief background on geological remote sensing and describe a suite of enhanced images using the barest earth Landsat mosaic for mapping surface mineralogy and geochemistry. These geological enhanced images provide improved inputs for predictive modelling of soil and rock properties over the Australian continent. In one case study, use of these products instead of existing Landsat TM band data to model chromium and sodium distribution using a random forest machine learning algorithm improved model performance by 28–46%.

<div>Exploring for the Future (2016-2024) was an Australian Government program led by Geoscience Australia (GA), in partnership with state and Northern Territory governments. The program aim was to drive industry investment in resource exploration in frontier regions of Australia by providing new precompetitive data and information about energy, mineral and groundwater resource potential. To address this overarching objective, GA led a key element of the Australian Government’s commitment to achieve net zero by 2050. The energy transition to an ever decreasing carbon emission economy will involve the increasing use of hydrogen gas (hydrogen). The key benefit of using hydrogen is that it is a clean fuel, emitting only water vapour and heat when combusted. However, hydrogen today is manufactured at a relatively high cost. The recent discovery of a 98% per cent pure natural hydrogen field in Mali (Africa) has led to low cost hydrogen production. It has also captured the imagination of explorers and the search is now on for new natural hydrogen accumulations across the world. Australia is considered one of the most prospective locations for sub-surface natural hydrogen due to our ancient geology and potential presence of suitable hydrogen traps. A review of occurrences of hydrogen in natural sub-surface rocks found high concentrations of hydrogen in central western, New South Wales (NSW). Helium is extracted in commercial quantities from natural gas and Australia currently has no local production. This project, in collaboration with the Geological Survey of NSW (GSNSW), built on the desktop review and has identified new occurrences of natural hydrogen and helium through soil gas surveys in various locations across central and far west, NSW. To support the Exploring for the Future program, six soil gas surveys for natural hydrogen and helium were jointly undertaken by staff from GA and GSNSW across central and western NSW during 2022-23. The project also included sites near the Tumut township to test various soil gas sampling techniques as well as the major focus in the Curnamona Province and Delamerian Orogen in far west New South Wales. In the first phase of the project, conceptual geological models for natural hydrogen and helium generation and accumulation were developed using pre-existing geoscientific data, including electromagnetic, magnetotelluric, magnetic, gravity, radiometric, drilling, seismic and satellite-derived data. The selected sites represented various concepts for natural hydrogen and helium generation, such as granite rocks rich in potassium, thorium, and uranium, banded iron formations, ultramafic rocks, and diatremes. The second phase of the project was the collection of soil gases from shallow (1 m deep) installations and subsequent molecular and isotopic compositional analysis at the GA Laboratory. Maximum hydrogen and helium concentrations in the soil gases are 309.5 ppm and 35.3 ppm, respectively, which is comparable to and even exceeds previously reported soil gas surveys both in Western Australia and overseas. The final phase was the integration of all datasets within a GIS platform for the interpretation and presentation of maps within this report.</div>