<div>Alluvial sediments have long been used in geochemical surveys as their compositions are assumed to be representative of areas upstream. Overbank and floodplain sediments, in particular, are increasingly used for regional to continental-scale geochemical mapping. However, during downstream transport, sediments from heterogeneous source regions are carried away from their source regions and mixed. Consequently, using alluvial sedimentary geochemical data to generate continuous geochemical maps remains challenging. In this study we demonstrate a technique that numerically unmixes alluvial sediments to make a geochemical map of their upstream catchments. The unmixing approach uses a model that predicts the concentration of elements in downstream sediments, given a map of the drainage network and element concentrations in the source region. To unmix sedimentary chemistry, we seek the upstream geochemical map that, when mixed downstream, best fits geochemical observations downstream. To prevent overfitting we penalise the roughness of the geochemical model. To demonstrate our approach we apply it to alluvial samples gathered as part of the Northern Australia Geochemical Survey. This survey gathered samples collected over a ∼ 500,000 km2 area in northern Australia. We first validate our approach for this sample distribution with synthetic tests, which indicate that we can resolve geochemical variability at scales greater than 0.5 – 1◦ in size. We proceed to invert real geochemical data from the total digestion of fine-grained fraction of alluvial sediments. The resulting geochemical maps for two elements of potential economic interest, Cu and Nd, are evaluated in detail. We find that in both cases, our predicted downstream concentrations match well against a held-out, unseen subset of the data, as well as against data from an independent geochemical survey. By performing principal component analysis on maps generated for all 46 available elements we produce a synthesis map showing the significant geochemical domains of this part of northern Australia. This map shows strong spatial similarities to the underlying lithological map of the area. Finally, we compare the results from our approach to a geochemical map produced by kriging. We find that, unlike the method presented here, kriging generates geochemical maps that are both dampened relative to expected magnitude, as well as being spatially distorted. We argue that the unmixing approach is the most appropriate method for generating geochemical maps from regional-scale alluvial surveys.&nbsp;</div> <b>Citation:</b> Alex G. Lipp, Patrice de Caritat, Gareth G. Roberts, Geochemical mapping by unmixing alluvial sediments: An example from northern Australia, <i>Journal of Geochemical Exploration,</i> Volume 248, <b>2023</b>, 107174, ISSN 0375-6742, https://doi.org/10.1016/j.gexplo.2023.107174. (https://www.sciencedirect.com/science/article/pii/S0375674223000213)

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>

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.

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 Operational Land Image (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. <b>Value: </b>Has broad application in mapping surface geochemistry and mineralogy of exposed soil and bedrock. Has applications in geological mapping and natural resource management including mapping of soil characteristics. <b>Scope: </b>Two enhanced bare earth products have been generated reflecting different Landsat satellites and acquisition periods. The first only uses Landsat 8 observations from 2013 to 2018. The second incorporates the full 30+ year archive combining Landsat 5, 7, and 8 from 1986 to 2018.

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%. <b>Citation:</b> Wilford, J. and Roberts, D., 2020. Enhanced barest earth Landsat imagery for soil and lithological modelling. 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.

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

<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%.

The National Geochemical Survey of Australia (NGSA) is Australia’s first national-scale geochemical survey. It was delivered to the public on 30 June 2011, after almost five years of stakeholder engagement, strategic planning, sample collection, preparation and analysis, quality assurance/quality control, and preliminary data analytics. The project was comprehensively documented in seven initial open-file reports and six data and map sets, followed over the next decade by more than 70 well-cited scientific publications. This review compiles the body of work and knowledge that emanated from the project to-date as an indication of the impact the NGSA had over the decade 2011-2021. The geochemical fabric of Australia as never seen before has been revealed by the NGSA. This has spurred further research and stimulated the mineral exploration industry. This paper also critically looks at operational decisions taken at project time (2007-2011) that were good and perhaps – with the benefit of hindsight – not so good, with the intention of providing experiential advice for any future large-scale geochemical survey of Australia or elsewhere. Strengths of the NGSA included stakeholder engagement, holistic approach to a national survey, involvement of other geoscience agencies, collaboration on quality assurance with international partners, and targeted promotion of results. Weaknesses included gaining successful access to all parts of the nation, and management of sample processing in laboratories. <b>Citation:</b> Patrice de Caritat; The National Geochemical Survey of Australia: review and impact. <i>Geochemistry: Exploration, Environment, Analysis </i>2022;; 22 (4): geochem2022–032. doi: https://doi.org/10.1144/geochem2022-032 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

<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 (NGSA; www.ga.gov.au/ngsa). The present study targeted the Yilgarn geological region in southwestern Australia. The samples were mostly taken from a depth of ~60-80 cm (Bottom Outlet Sediments, BOS) in floodplain deposits at or near the outlet of large catchments (drainage basins). A small number of surface (0-10 cm) samples (Top Outlet Sediments, TOS) were also included in the study. For all, a coarse grain-size fraction (<2 mm) was air-dried, sieved, milled then digested (hydrofluoric acid + nitric acid followed by aqua regia) to release total strontium. Overall, 107 NGSA BOS < 2 mm and 13 NGSA TOS < 2 mm were analysed for Sr isotopes. Given that there are ~10 % field duplicates in the NGSA, all those samples originate from within 97 NGSA catchments, which together cover 533 000 km2 of southwestern Australia. Preliminary results for the BOS samples demonstrate a wide range of strontium isotopic values (0.7152 < 87Sr/86Sr < 1.0909) 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 (>100 km) patterns that appear to be consistent, in many places, with surface geology, regolith/soil type and/or nearby outcropping bedrock. For instance, catchments in the western and central Yilgarn dominated by felsic intrusive basement geology have radiogenic 87Sr/86Sr signatures in the floodplain sediments consistent with published whole-rock data. Similarly, unradiogenic signatures in sediments in the eastern Yilgarn are in agreement with published whole-rock data. 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>