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Strontium isotopes (87Sr/86Sr) are useful to trace processes in the Earth sciences as well as in forensic, archaeological, palaeontological, and ecological sciences. As very few large-scale Sr isoscapes exist in Australia, we have identified an opportunity to determine 87Sr/86Sr ratios on archived fluvial sediment samples from the low-density National Geochemical Survey of Australia. The present study targeted the northern parts of Western Australia, the Northern Territory, and Queensland, north of 21.5∘ S. The samples were taken mostly from a depth of ∼60–80 cm in floodplain deposits at or near the outlet of large catchments (drainage basins). A coarse ( mm) grain-size fraction was air-dried, sieved, milled, and digested (hydrofluoric acid + nitric acid followed by aqua regia) to release total Sr. The Sr was then separated by chromatography, and the 87Sr/86Sr ratio was determined by multicollector inductively coupled plasma mass spectrometry. The results demonstrate a wide range of Sr isotopic values (0.7048 to 1.0330) over the survey area, reflecting a large diversity of source rock lithologies, geological processes, and bedrock ages. The spatial distribution of 87Sr/86Sr shows coherent (multi-point anomalies and smooth gradients), large-scale (>100 km) patterns that appear to be broadly consistent with surface geology, regolith/soil type, and/or nearby outcropping bedrock. For instance, the extensive black clay soils of the Barkly Tableland define a >500 km long northwest–southeast-trending unradiogenic anomaly (87Sr/86Sr ). Where sedimentary carbonate or mafic/ultramafic igneous rocks dominate, low to moderate 87Sr/86Sr values are generally recorded (medians of 0.7387 and 0.7422, respectively). Conversely, In proximity to the outcropping Proterozoic metamorphic basement of the Tennant, McArthur, Murphy, and Mount Isa geological regions, radiogenic 87Sr/86Sr values (>0.7655) are observed. A potential correlation between mineralization and elevated 87Sr/86Sr values in these regions needs to be investigated in greater detail. Our results to date indicate that incorporating soil/regolith Sr isotopes in regional, exploratory geoscience investigations can help identify basement rock types under (shallow) cover, constrain surface processes (e.g. weathering and dispersion), and, potentially, recognize components of mineral systems. Furthermore, the resulting Sr isoscape and future models derived therefrom can also be utilized in forensic, archaeological, palaeontological, and ecological studies that aim to investigate, for example, past and modern animal (including humans) dietary habits and migrations. The new spatial Sr isotope dataset for the northern Australia region is publicly available (de Caritat et al., 2022a; https://doi.org/10.26186/147473).

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 $\sim$ 500,000 km$^2$ 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$^{\circ}$ 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, previously `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 to identify the significant geochemical domains in 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 geochemical maps produced by interpolating alluvial geochemical data using two standard methods: kriging and inverse distance weighting. We find that, unlike the method presented here, both these methods can generate maps that are 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.

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 km

With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates that affect soil formation to predict and map aqua-regia-extractable Li content across the 7.6×106 km2 area of Australia. Catchment outlet sediment samples (i.e. soils formed on alluvial parent material) were collected by the National Geochemical Survey of Australia at 1315 sites, with both top (0–10 cm depth) and bottom (on average ∼60–80 cm depth) catchment outlet sediments sampled. We developed 50 bootstrap models using a cubist regression tree algorithm for each depth. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction with a root mean square error of 3.32 mg kg−1 (which is 44.2 % of the interquartile range) for the top depth. The model for the bottom depth has yet to be validated. The variables of importance for the models indicated that the first three Landsat 30+ Barest Earth bands (red, green, blue) and gamma radiometric dose have a strong impact on the development of regression-based Li prediction. The bootstrapped models were then used to generate digital soil Li prediction maps for both depths, which could identify and delineate areas with anomalously high Li concentrations in the regolith. The predicted maps show high Li concentration around existing mines and other potentially anomalous Li areas that have yet to be verified. The same mapping principles can potentially be applied to other elements. The Li geochemical data for calibration and validation are available from de Caritat and Cooper (2011b; https://doi.org/10.11636/Record.2011.020) and Main et al. (2019; https://doi.org/10.11636/Record.2019.002), respectively. The covariate data used for this study were sourced from the Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government's National Collaborative Research Infrastructure Strategy (NCRIS; https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/, last access: 6 December 2022; TERN, 2019). The final predictive map is available at https://doi.org/10.5281/zenodo.7895482 (Ng et al., 2023).

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 2011–21. 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 (2007–11) 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. We conclude that the NGSA was a low-cost, high-benefit project that delivered scientific excellence, promoted stakeholder engagement, demonstrated national leadership, and contributed to enhanced national resource wealth. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping

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 magnetic susceptibility (MS), 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 performed on the survey's 268 topsoil samples (0–5 cm depth; 1 sample per km2). Principal components (PCs) are calculated after imputation of censored data and centered log‐ratio transformation. The sequential provenancing approach is underpinned by (i) the preparation of interpolated raster grids of the soil properties (including PCs); (ii) the explicit quantification and propagation of uncertainty; (iii) the intersection of the soil property rasters with the values of the evidentiary sample (± uncertainty); and (iv) the computation of cumulative provenance rasters (“heat maps”) for the various analytical techniques. The sequential 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 and MS (mineralogy), as well as XRF and total ICP‐MS (geochemistry) analytical methods, offer the most precise and accurate provenance predictions. Inclusion of PCs in provenancing adds marginally to the performance. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations and gaps, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications.

The values and distribution patterns of the strontium (Sr) isotope ratio 87Sr/86Sr in Earth surface materials are of use in the geological, environmental, and social sciences. Ultimately, the 87Sr/86Sr ratios of soils and everything that lives in and on them are inherited from the rocks that are the parent materials of the soil's components. In Australia, there are few large-scale surveys of 87Sr/86Sr available, and here we report on a new, low-density dataset using 112 catchment outlet (floodplain) sediment samples covering 529 000 km2 of inland southeastern Australia (South Australia, New South Wales, Victoria). The coarse (<2 mm) fraction of bottom sediment samples (depth ∼ 0.6–0.8 m) from the National Geochemical Survey of Australia were milled and fully digested before Sr separation by chromatography and 87Sr/86Sr determination by multicollector-inductively coupled plasma mass spectrometry. The results show a wide range of 87Sr/86Sr values from a minimum of 0.7089 to a maximum of 0.7511 (range 0.0422). The median 87Sr/86Sr (± median absolute deviation) is 0.7199 (± 0.0071), and the mean (± standard deviation) is 0.7220 (± 0.0106). The spatial patterns of the Sr isoscape observed are described and attributed to various geological sources and processes. Of note are the elevated (radiogenic) values (≥∼ 0.7270; top quartile) contributed by (1) the Palaeozoic sedimentary country rock and (mostly felsic) igneous intrusions of the Lachlan geological region to the east of the study area; (2) the Palaeoproterozoic metamorphic rocks of the central Broken Hill region; both these sources contribute radiogenic material mainly by fluvial processes; and (3) the Proterozoic to Palaeozoic rocks of the Kanmantoo, Adelaide, Gawler, and Painter geological regions to the west of the area; these sources contribute radiogenic material mainly by aeolian processes. Regions of low 87Sr/86Sr (≤∼ 0.7130; bottom quartile) belong mainly to (1) a few central Murray Basin catchments; (2) some Darling Basin catchments in the northeast; and (3) a few Eromanga geological region-influenced catchments in the northwest of the study area; these sources contribute unradiogenic material mainly by fluvial processes. The new spatial Sr isotope dataset for the DCD (Darling–Curnamona–Delamerian) region is publicly available (de Caritat et al., 2022; https://dx.doi.org/10.26186/146397)​​​​​​​.

Mineral deposits are metal enrichment anomalies, occurring as local manifestations of the interplay between various geological processes that operate at a wide range of temporal and spatial scales. Mineral prospectivity maps are generated by integrating several proxy maps that represent critical geological processes in a mineral system conceptual model. The derivation of mineral prospectivity maps is subject to several types of uncertainty, including systematic (inadequate knowledge of mineralisation processes), stochastic (incomplete geoscience data), and model uncertainty (multiple choices for predictive models and their parameters). Traditional approaches to mineral prospectivity mapping often fail to fully appreciate different sources of uncertainty and spatiotemporal interdependencies between proxy maps associated with the mineral system components. Therefore, these traditional approaches are biased and understate the overall uncertainty. For instance, spatial proxies are mapped using univariate deterministic approaches that ignore stochastic uncertainty and spatial dependencies (i.e., auto- and cross-correlations). This study presents a multivariate stochastic model for prediction and uncertainty quantification of mineral exploration targets by combining multivariate geostatistical simulations and spatial machine learning algorithms. The spatial machine learning algorithm used in the stochastic model is a spatially aware random forests algorithm based on higher-order spatial statistics. It is demonstrated that the proposed approach can detect intrinsic heterogeneity, spatial interdependencies, and complex spatial patterns in proxy maps that are related to the mineralisation type of interest. The approach is illustrated using a synthetic case study with multiple geochemical, geophysical, and lithological attributes.

Heavy minerals (HMs) are minerals with a specific gravity greater than 2.9 g/cm3. They are commonly highly resistant to physical and chemical weathering, and therefore persist in sediments as lasting indicators of the (former) presence of the rocks they formed in. The presence/absence of certain HMs, their associations with other HMs, their concentration levels, and the geochemical patterns they form in maps or 3D models can be indicative of geological processes that contributed to their formation. Furthermore trace element and isotopic analyses of HMs have been used to vector to mineralisation or constrain timing of geological processes. The positive role of HMs in mineral exploration is well established in other countries, but comparatively little understood in Australia. Here we present the results of a pilot project that was designed to establish, test and assess a workflow to produce a HM map (or atlas of maps) and dataset for Australia. This would represent a critical step in the ability to detect anomalous HM patterns as it would establish the background HM characteristics (i.e., unrelated to mineralisation). Further the extremely rich dataset produced would be a valuable input into any future machine learning/big data-based prospectivity analysis. The pilot project consisted in selecting ten sites from the National Geochemical Survey of Australia (NGSA) and separating and analysing the HM contents from the 75-430 µm grain-size fraction of the top (0-10 cm depth) sediment samples. A workflow was established and tested based on the density separation of the HM-rich phase by combining a shake table and the use of dense liquids. The automated mineralogy quantification was performed on a TESCAN® Integrated Mineral Analyser (TIMA) that identified and mapped thousands of grains in a matter of minutes for each sample. The results indicated that: (1) the NGSA samples are appropriate for HM analysis; (2) over 40 HMs were effectively identified and quantified using TIMA automated quantitative mineralogy; (3) the resultant HMs’ mineralogy is consistent with the samples’ bulk geochemistry and regional geological setting; and (4) the HM makeup of the NGSA samples varied across the country, as shown by the mineral mounts and preliminary maps. Based on these observations, HM mapping of the continent using NGSA samples will likely result in coherent and interpretable geological patterns relating to bedrock lithology, metamorphic grade, degree of alteration and mineralisation. It could assist in geological investigations especially where outcrop is minimal, challenging to correctly attribute due to extensive weathering, or simply difficult to access. It is believed that a continental-scale HM atlas for Australia could assist in derisking mineral exploration and lead to investment, e.g., via tenement uptake, exploration, discovery and ultimately exploitation. As some HMs are hosts for technology critical elements such as rare earth elements, their systematic and internally consistent quantification and mapping could lead to resource discovery essential for a more sustainable, lower-carbon economy.

Prediction of true classes of surficial and deep earth materials using multivariate geospatial data is a common challenge for geoscience modellers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. To explore these patterns, advanced spatial data analysis techniques should be implemented. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith that fully accounts for spatial uncertainty. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest (RF) predictive model is trained and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation). The simulated results are subsequently back-transformed to the original compositional space. The trained RF model is used to predict the true classes for simulated compositions at unsampled locations. The proposed approach is illustrated through two case studies. In the first case study the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.

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.

The log ratio methodology converts compositional data, such as concentrations of chemical elements in a rock, from their original Aitchison geometry to interpretable real orthonormal coordinates, thereby allowing meaningful statistical processing and visualization. However, it must be taken into account that the original concentrations can be flawed by detection limit or imprecision problems that can severely affect the resulting coordinates. This paper aims to construct such orthonormal log ratio coordinates, called weighted pivot coordinates, that capture the relevant relative information about an original component and treat the redundant information in a controlled manner. Theoretical developments are supported by a thorough simulation study. Weighted pivot coordinates are then applied to the geochemical mapping of catchment outlet sediments from the National Geochemical Survey of Australia illustrating their advantage over possible alternatives.

During the National Geochemical Survey of Australia over 1300 top (0–10cm depth) and bottom (~60–80cm depth) sediment samples (including ~10% field duplicates) were collected from the outlet of 1186 catchments covering 81% of the continent at an average sample density of 1 site/5200km2. The

The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time. Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km 2 ), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations. Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation methods. Furthermore, insights can be gained into the landscape processes controlling element concentration, distribution and mobility from analysis of the covariates used in the model. This modelling approach can be extended to groups of elements (indices), element ratios, isotopes or mineralogy over a range of scales and in a variety of environments.

Single component geochemical maps are the most basic representation of spatial elemental distributions and commonly used in environmental and exploration geochemistry. However, the compositional nature of geochemical data imposes several limitations on how the data should be presented. The problems relate to the constant sum problem (closure), and the inherently multivariate relative information conveyed by compositional data. Well known is, for instance, the tendency of all heavy metals to show lower values in soils with significant contributions of diluting elements (e.g., the quartz dilution effect); or the contrary effect, apparent enrichment in many elements due to removal of potassium during weathering. The validity of classical single component maps is thus investigated, and reasonable alternatives that honour the compositional character of geochemical concentrations are presented. The first recommended such method relies on knowledge-driven log-ratios, chosen to highlight certain geochemical relations or to filter known artefacts (e.g. dilution with SiO 2 or volatiles). This is similar to the classical normalisation approach to a single element. The second approach uses the (so called) log-contrasts, that employ suitable statistical methods (such as classification techniques, regression analysis, principal component analysis, and clustering of variables) to extract potentially interesting geochemical summaries. The caution from this work is that if a compositional approach is not used, it becomes difficult to guarantee that any identified pattern, trend or anomaly is not an artefact of the constant sum constraint. In summary the authors recommend a chain of enquiry that involves searching for the appropriate statistical method that can answer the required geological or geochemical question whilst maintaining the integrity of the compositional nature of the data. The required log-ratio transformations should be applied followed by the chosen statistical method. Interpreting the results may require a closer working relationship between statisticians, data analysts and geochemists.

We identify and understand the diverse nature of Ni mineralisation across the Australian continent using Self-Organising Maps, an unsupervised clustering algorithm. We integrate remotely sensed, continental-scale multivariate geophysical/mineralogical data and combine the outputs of our machine learning analysis with Ni mineral occurrence data. The resulting Ni prospectivity map identifies the location of Ni mines with an accuracy 92.58%. We divide areas of prospective Ni mineralisation into five clusters. These clusters indicate subtle but significant differences in regolith and bedrock geophysical/mineralogical footprints of Ni sulphide and Ni laterite deposits. This information is used to identify and understand the nature of potential Ni targets in regions where prospective bedrock mineralisation is concealed by regolith materials. Our machine learning approach can be applied to the analysis of other mineral commodities and at local-/prospect scales.

Plutonium (Pu) interactions in the environment are highly complex. Site-specific variables play an integral role in determining the chemical and physical form of Pu, and its migration, bioavailability, and immobility. This paper aims to identify the key variables that can be used to highlight regions of radioecological sensitivity and guide remediation strategies in Australia. Plutonium is present in the Australian environment as a result of global fallout and the British nuclear testing program of 1952–1958 in central and west Australia (Maralinga and Montebello islands). We report the first systematic measurements of 239+240Pu and 238Pu activity concentrations in distal (≥1000 km from test sites) catchment outlet sediments from Queensland, Australia. The average 239+240Pu activity concentration was 0.29 mBq.g −1 (n = 73 samples) with a maximum of 4.88 mBq.g −1.238Pu/239+240Pu isotope ratios identified a large range (0.02–0.29 (RSD: 74%)) which is congruent with the heterogeneous nuclear material used for the British nuclear testing programme at Maralinga and Montebello Islands. The use of a modified PCA relying on non-linear distance correlation (dCorr) provided broader insight into the impact of environmental variables on the transport and migration of Pu in this soil system. Primary key environmental indicators of Pu presence were determined to be actinide/lanthanide/heavier transition metals, elevation, electrical conductivity (EC), CaO, SiO2, SO3, landform, geomorphology, land use, and climate explaining 81.7% of the variance of the system. Overall this highlighted that trace level Pu accumulations are associated with the coarse, refractive components of Australian soils, and are more likely regulated by the climate of the region and overall soil type.

The GEMAS (geochemical mapping of agricultural soil) project collected 2108 Ap horizon soil samples from regularly ploughed fields in 33 European countries, covering 5.6 million km(2). The

The mineral exploration industry is used to very high sample densities (100s to 1000s of samples/km 2 ) for geochemical exploration in order to define drill targets. Lately, geoscience organizations in many countries have been geochemically mapping increasingly larger areas at progressively lower sampling densities (1 site/100 to 1 site/18 000 km 2 ). A single ore body is too small a target and cannot be expected to be discovered at such low sample densities; indeed numerous deposits could be hidden within a 100 × 100 km grid cell. However, mineral systems, which include all geological ingredients and processes necessary for the generation of mineral deposits, form much larger targets that could be identified even at such low sampling densities. Examples from some European low density geochemical surveys where patterns emerged that may have implications for mineral exploration are shown and discussed. It is concluded that low density geochemical mapping holds great promise in the early stages of mineral exploration programmes in guiding subsequent effort into the more fertile regions. Interpretation of these maps, however, may need a different approach than that used in classical, high density mapping exercises, where only ‘high values’ of certain metals are the traditional target.

Catchment/floodplain sediment geochemical datasets from three continental-scale geochemical projects in Australia (AU), China (CH) and Europe (EU) were studied by comparing sample media, sample preparation, elements determined, analytical methods, detection limits and proportions of reportable values, and certified reference materials used. Twenty six elements (Ba, Ce, Co, Cr, Cu, Mo, Nb, Ni, Pb, Rb, Sr, Th, V, Y, Zn, Zr, Al2O3, CaO, K2O, MgO, MnO, Na2O, P2O5, SiO2, Fe2O3 (total), TiO2) were considered comparable among the 35 determined by all three projects, according to the criteria for global comparability of elements proposed in this paper. The median concentrations of all elements (except Co, Y, Zr, SiO2 and TiO2) in the three datasets increase in the following order: AU

Geochemical surveys collect sediment or rock samples, measure the concentration of chemical elements, and report these typically either in weight percent or in parts per million (ppm). There are usually a large number of elements measured and the distributions are often skewed, containing many potential outliers. We present a new robust principal component analysis (PCA) method for geochemical survey data, that involves first transforming the compositional data onto a manifold using a relative power transformation. A flexible set of moment assumptions are made which take the special geometry of the manifold into account. The Kent distribution moment structure arises as a special case when the chosen manifold is the hypersphere. We derive simple moment and robust estimators (RO) of the parameters which are also applicable in high-dimensional settings. The resulting PCA based on these estimators is done in the tangent space and is related to the power transformation method used in correspondence analysis. T...

Catchment/floodplain sediment geochemical datasets from three continental-scale geochemical projects in Australia (AU), China (CH) and Europe (EU) were studied by comparing sample media, sample preparation, elements determined, analytical methods, detection limits and proportions of reportable values, and certified reference materials used. Twenty six elements (Ba, Ce, Co, Cr, Cu, Mo, Nb, Ni, Pb, Rb, Sr, Th, V, Y, Zn, Zr, Al 2 O 3 , CaO, K 2 O, MgO, MnO, Na 2 O, P 2 O 5 , SiO 2 , Fe 2 O 3 (total), TiO 2 ) were considered comparable among the 35 determined by all three projects, according to the criteria for global comparability of elements proposed in this paper. The median concentrations of all elements (except Co, Y, Zr, SiO 2 and TiO 2 ) in the three datasets increase in the following order: AU 2 O, MgO and Zn are generally greater than 2 (and up to 7), which could be explained partly by protracted weathering in Australia, and partly by the smaller grain size fraction analysed of the CH dataset (

The acquisition and collation of large numbers of spectral measurements of samples requires the strict adherence to set protocols to ensure the integrity of the data. Errors can occur in the measurement or processing that may not initially be detected and would require the time-consuming process of re-measuring of samples. This paper outlines the development of a national spectral library, and, the associated protocols for the systematic acquisition, quality control and processing of the spectral measurements of a collection of over 1300 soil and powders from across Australia using portable field spectroradiometers and laboratory Fourier transform infrared spectrometers. The processed spectra were uploaded to the CSIRO Data Access Portal, along with the metadata of the samples and measurements. These data are now publicly available as a national spectral library for the benefit of the research and user community.

A new method for detecting and quantifying diffuse contamination at the continental to regional scale is based on the analysis of cumulative distribution functions (CDFs). It uses cumulative probability (CP) plots for spatially representative data sets, preferably containing1000 determinations. Simulations demonstrate how different types of contamination influence elemental CDFs of different sample media. It is found that diffuse contamination is characterized by a distinctive shift of the low-concentration end of the distribution of the studied element in its CP plot. Diffuse contamination can be detected and quantified via either (1) comparing the distribution of the contaminating element to that of an element with a geochemically comparable behavior but no contamination source (e.g., Pb vs Rb), or (2) comparing the top soil distribution of an element to the distribution of the same element in subsoil samples from the same area, taking soil forming processes into consideration. Both procedures are demonstrated for geochemical soil data sets from Europe, Australia, and the U.S.A. Several different data sets from Europe deliver comparable results at different scales. Diffuse Pb contamination in surface soil is estimated to be0.5 mg/kg for Australia, 1-3 mg/kg for Europe, and 1-2 mg/kg, or at least5 mg/kg, for the U.S.A. The analysis presented here also allows recognition of local contamination sources and can be used to efficiently monitor diffuse contamination at the continental to regional scale.

The current (2005–2011) status of the chemical composition of snow cover and rain collected at a height of 1.5 m above ground was studied within 11 km around the Severonikel industrial complex, one of the largest SO2 and metal contamination sources in N Europe. In spite of a significant decrease in emissions during the past 20 years, Ni and Cu concentrations in snow remain extremely high near the source (2500 and 1500 times background values, respectively). Although showing a five- to six-fold decrease in Ni and Cu concentrations since 1994, rain water currently still has concentrations 150 and 80 times background, respectively. Differences in the chemical composition of snow pack and rain collected at a height of 1.5 m above ground in this case are not caused by seasonal effects, but rather by the height of precipitation sampling relative to the ground.

In this paper, the geochemical composition of surficial regolith is statistically analysed and compared to independent geoscientific datasets to infer processes governing regolith composition. Surface (0–10 cm depth) and sub-surface (∼60–80 cm depth) transported sediment samples from the National Geochemical Survey of Australia were analysed for total element content in both coarse ( Censored data (

Thallium is released into the biosphere from both natural and anthropogenic sources. It is generally present in the environment at low levels; however, human activity has greatly increased its content. Atmospheric emission and deposition from industrial sources have resulted in increased concentrations of thallium in the vicinity of mineral smelters and coal-burning facilities. Increased levels of thallium are found in vegetables, fruit and farm animals. Thallium is toxic even at very low concentrations and tends to accumulate in the environment once it enters the food chain. Thallium and thallium-based compounds exhibit higher water solubility compared to other heavy metals. They are therefore also more mobile (e.g. in soil), generally more bioavailable and tend to bioaccumulate in living organisms. The main aim of this review was to summarize the recent data regarding the actual level of thallium content in environmental niches and to elucidate the most significant sources of thallium in the environment. The review also includes an overview of analytical methods, which are commonly applied for determination of thallium in fly ash originating from industrial combustion of coal, in surface and underground waters, in soils and sediments (including soil derived from different parent materials), in plant and animal tissues as well as in human organisms.

The Earth’s surface comprises minerals diagnostic of weathering, deposition and erosion. The first continental-scale mineral maps generated from an imaging satellite with spectral bands designed to measure clays, quartz and other minerals were released in 2012 for Australia. Here we show how these satellite mineral maps improve our understanding of weathering, erosional and depositional processes in the context of changing weather, climate and tectonics. The clay composition map shows how kaolinite has developed over tectonically stable continental crust in response to deep weathering during northwardly migrating tropical conditions from 45 to 10 Ma. The same clay composition map, in combination with one sensitive to water content, enables the discrimination of illite from montmorillonite clays that typically develop in large depositional environments over thin (sinking) continental crust such as the Lake Eyre Basin. Cutting across these clay patterns are sandy deserts that developed

Plots containing common variables are prone to induced correlations. These plots are commonly used in hydrochemistry and other branches of geochemistry with no discussion of the potential influence of the common variables on the observed trends. Some examples from the literature include: (1) Comparison of a sum to a component of the sum. (2) Comparison of a ratio to its own numerator or denominator. (3) Comparison of two ratios with the same numerator or denominator. These types of common variable plots may increase or decrease the correlation that exists between two independent variables. This paper presents a randomisation method for distinguishing between the “real” and induced relationships that can occur in plots of type 1, 2 or 3 above, thereby enabling a distinction between hydrochemical processes and induced correlation(s). If the slope and correlation coefficient of the actual data are similar to the randomised data, the relationship cannot be attributed to a hydrochemical process alone. If the slope and correlation coefficient are different, it is likely that the data trend is the result of a real process, but we recommend this be confirmed using additional evidence such as relationships between other element/ion concentrations or independent isotopic evidence. Common variable plots are useful tools for identifying and describing hydrochemical processes, but the data trends need to be tested to ensure that correlations are a result of real processes rather than an artefact caused by a common variable.

The Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC) Otway Project in the onshore Otway Basin, Victoria, is Australia’s first pilot project for the long term sequestration of CO2. The Otway Project has injected 65,445 tonnes of a mixed CO2-CH4 supercritical fluid (77 mol% CO2, 20 mol% CH4, 3 mol% of minor wet gases and N2) some 2000 m below the surface into the Waarre Formation, which is capped by the Belfast Mudstone regional seal. The site has been comprehensively characterised by a multidisciplinary team and the risk analysis has shown the likelihood of leakage out of the injection horizon–let alone to the land surface–to be exceedingly low. Nevertheless, the objectives of the CO2CRC through the Otway Project are not only to demonstrate safe CO2 injection, but also to develop new methodologies for monitoring and verification (M&V) of carbon storage that might apply to future commercial scale injection. At Otway, this involves M&V at the reservoir level and Assurance Monitoring, in the shallow subsurface (aquifers and soils) and the atmosphere. The groundwater monitoring system represents the most comprehensive system for monitoring freshwater in the vicinity of a CO2 storage demonstration to date. Monitoring the groundwater is of particular significance in demonstrating the ongoing integrity of natural resources to the general community.The first benchmark of any monitoring system is the establishment of a baseline from which to define natural variations in the system prior to injection. At Otway the natural baseline is complex due to the influence of the Port Campbell Limestone immediately below the soil zone. Since June 2006, groundwater levels and composition have been monitored at 21 bores completed in the unconfined Port Campbell Limestone aquifer, and 3 bores completed in the deeper confined Dilwyn aquifer. All monitoring wells are located within 10 km of the CRC-1 injection well. The groundwater program includes (i) biannual geochemical sampling to establish groundwater composition; (ii) monitoring of water levels to determine seasonal variations to formation water flow rate and direction; and (iii) headspace gas sampling of the Dilwyn aquifer to detect tracer compounds and CO2 content. This monitoring is necessary to establish the connectivity and fluid migration timescales of the fresh water aquifers and to understand the water-rock interactions and resulting fluid composition.The assurance monitoring team works closely with government agencies and landowners and provides data as required. Community engagement has been vital to the success of the program and considerable effort goes toward keeping landowners informed of our activities and minimizing disruption to their properties. To date, the groundwater component of the assurance monitoring program has not detected any perturbation in the baseline condition as a consequence of CO2 injection. It is important to note that this does not necessarily indicate containment; it does however provide assurance to the community and regulators that the natural resources have not been impacted by the CCS activities and can continue to be used safely.

As part of mineral exploration campaigns carried out by Anglo American plc in India, groundwater samples were collected from over 200 wells in the Hinta and Kayar sedimentary exhalative base metal districts to evaluate the usefulness of hydrogeochemistry in mineral exploration. Mineralization occurs here mostly as Pb and Zn sulphides in metamorphic silicic–felsic and carbonate basement rocks. Groundwater chemical and isotopic data from 27 wells at Hinta and 181 wells at Kayar were interpreted using major element concentrations, their ratios to a conservative element (e.g. Cl − ), isotope systematics and thermodynamic modelling. The results indicate that the following processes take place: evaporation, evapotranspiration, water–regolith–rock interaction (e.g. weathering of carbonate, silicate, and sulphate or sulphide minerals) and water mixing. Certain gradients, rather than absolute values, may reflect proximity to mineralization; these include: (1) concentrations of ore and related elements normalized to a conservative element (e.g. Zn/Cl − molar ratios from 1 × 10 −7 up to 2 × 10 −2 ; SO 4 2 − /Cl − molar ratios from 1 × 10 −5 up to 3.3); (2) isotopic ratios (e.g. δ 34 S from +23 down to +5‰ V-CDT, with two samples at −5‰); (3) saturation indices for ore, alteration or secondary minerals (e.g. SI jarosite from −15 up to zero). Hydrogeochemistry may be a useful tool to help vector toward ore deposits in deep or covered settings.

Published estimates for base metal emissions from the copper–nickel industry on the Kola Peninsula are re-examined in the light of (a) chemical data on the composition of the ores; (b) official emission figures for 1994; and (c) modelled emissions based on dry and wet deposition estimates derived from data for snow and rain samples collected in 1994. The modelled emissions, official emission figures and chemical data are mutually compatible for Ni, Cu and Co and show that previously published figures underestimated the emissions of the major elements, Ni and Cu (though within the same order of magnitude) and overestimated the emissions of As, Pb, Sb and Zn by up to several orders of magnitude, in some cases exceeding the calculated total input to the plants. Published estimates have neglected information on the nature and chemistry of the ores processed in metallurgical industries in the Noril'sk area of Siberia and the Urals. Revised emission estimates for 1994, using knowledge of the chemistry of the ores, are proposed: taken with published information on total emissions up to 2000 these data give an indication of emission levels in more recent years.

A series of regional pilot geochemical survey projects have been carried out in Australia over recent years. These pilot projects targeted a number of landscape and climate settings that are challenging for geochemical mapping, and applied increasingly lower sampling densities, from c . 1 site/300 km 2 to c . 1 site/ 2100 km 2 . Selected results are presented from pilot geochemical surveys in the Riverina, Gawler and Thomson regions, as well as results from modelling aimed at testing the robustness of ultra-low sampling densities in Australia. The salient conclusions are that: (1) geochemical surveys sampling surface and near-surface transported regolith are capable of identifying major lithologies and mineralization below the cover; and (2) a sampling strategy targeting outlet sediments of large catchments allows the application of ultra-low sampling densities in Australia. These findings overcome the main hurdles met by earlier attempts at designing a nation-wide geochemical survey. The strategy for the resulting National Geochemical Survey of Australia (NGSA) project, recently approved for funding under the Australian Government9s Onshore Energy Security Initiative, is outlined. By 2011, the NGSA will produce a publicly available multi-element, internally consistent and state-of-the-art regolith geochemical database and atlas to support decision-makers.

The supergiant Pb-Zn-Ag Broken Hill orebody and numerous other minor mineral deposits occur within the limited outcrop of the Proterozoic Curnamona Province of Australia. The vast majority of this Province is concealed by up to 200 m of transported regolith, hampering conventional exploration strategies. Approximately 300 groundwater samples were collected over the southern Curnamona Province to test whether this medium could be helpful in the search for hidden mineral deposits. Sulphur, Sr and Pb isotope composition of the groundwaters were determined and S excess (S XS ), i.e., the amount of S that can be ascribed neither to evaporation nor to mixing, was calculated. Many samples were recognised to have undergone an addition of 34 S-depleted S, which can be attributed to oxidation of sulfides with a Broken Hill type δ 34 S signature (average ∼0‰ V-CDT). Furthermore, Sr isotopes identify the broad types of bedrock that the groundwater has been interacting with, from the less radiogenic Adelaidean rocks (and minerals) in the west (groundwater 87 Sr/ 86 Sr ratio as low as 0.708) to the highly radiogenic Willyama Supergroup in the east ( 87 Sr/ 86 Sr ratio up to 0.737). The groundwaters have 207 Pb/ 204 Pb and 206 Pb/ 204 Pb ratios comparable to, or intermediate between, various mineralisation types recognised in the area (Broken Hill, Rupee, Thackaringa, etc., types). The few samples taken in the vicinity of known mineralisation yield positive indicators (positive S XS , low δ 34 S, 87 Sr/ 86 Sr signature of bedrock type and Pb isotope fingerprinting of mineralisation type). This study also highlights several new locations under sedimentary cover where these indicators suggest interaction with mineralisation.

The evolution of the Callabonna Sub-basin (central Australia) is described in terms of sediment provenance, stratigraphy, sedimentology, and postdepositional mineral and chemical alteration. Six mineral exploration drillholes in the Mundi Mundi Plain were studied using a combination of qualitative and quantitative XRD, PIMATM (portable infrared mineral analyzer), XRF, normative analysis, and SEM. The sedimentary cover consists of 160-210 m of diagenetically modified fluvio-lacustrine Tertiary clay, silt, and sand comprising sandy, mottled, and gray clay units overlain by Quaternary soil. The basement consists of weathered Proterozoic schist, gneiss, siltstone, and shale. Quartz, kaolinite, smectite, and muscovite are the dominant minerals within the sediments, whereas the weathered bedrock contains quartz, feldspar, kaolinite, illite, and muscovite. Characteristic wavelength absorption features from PIMATM show that the youngest sandy clay unit exhibits the lowest reflectance intensities because of its lower diagenetic maturity. Weathering of primary minerals within the basement produced an illite-kaolinite-rich saprolite whose clays exhibit strong reflectance intensities and well-developed crystallinity. The micromorphology of all secondary products reflects their environment of crystallization. Low porosity and anoxic conditions within the sediments resulted in poorly developed clays of variable textures, colors, and compositions. Conversely, the weathered bedrock developed over protracted periods of in-situ weathering resulting in better-developed secondary products. This study distinguishes between preburial weathering of the basement, detrital mineralogy, postdepositional alteration of the sedimentary cover, and subsequent weathering of previously formed mineral assemblages. This has widespread implications for paleotopographic(-weathering) reconstitution and landscape evolution thus: (1) the extent of weathering of basement relative to diagenesis and maturity of sediments and gradational nature of their unconformity suggests that most of the alteration of basement occurred prior to, and was sustained after, deposition of sediments, and (2) morphotectonic evolution of the region controlled the composition of transported materials by changing the style and patterns of fluvial systems, further influencing the distribution and preservation of sediments and weathered bedrock, and hence regional landscape morphology.