Multi-element near-surface geochemistry from the National Geochemical Survey of Australia has been evaluated in the context of mapping the exposed to deeply buried major crustal blocks of the Australian continent. The major crustal blocks, derived from geophysical and geological data, reflect distinct tectonic domains comprised of igneous, metamorphic and sedimentary rock assemblages representing a time scale ranging from early Archean to recent Cenozoic. The geochemical data have been treated as compositional data to uniquely describe and characterize the geochemistry of the major crustal blocks across Australia according to the following workflow: imputation of missing/censored data, log-ratio transformation, multivariate statistical analysis, classification/allocation, and multivariate geospatial (minimum/maximum autocorrelation factor) analysis. Using cross validation techniques the uniqueness of each major crustal block has been quantified. The abilities to predict (1) the membership, or lack thereof, of a surface regolith sample to one, or none, of the major crustal blocks, and (2) the major crustal block at sites that have not been sampled are demonstrated. In conclusion, the surface regolith of the Australian continent contains a geochemical record of the original crustal block composition, despite secondary modifications due to physical transport and chemical weathering effects. <b>Citation:</b> E.C. Grunsky, P. de Caritat, U.A. Mueller, Using surface regolith geochemistry to map the major crustal blocks of the Australian continent, <i>Gondwana Research</i>, Volume 46, 2017, Pages 227-239, ISSN 1342-937X, https://doi.org/10.1016/j.gr.2017.02.011

<div>The fluid inclusion stratigraphy database table contains publicly available results from Geoscience Australia's organic geochemistry (ORGCHEM) schema and supporting oracle databases for Fluid Inclusion Stratigraphy (FIS) analyses performed by FIT, a Schlumberger Company (and predecessors), on fluid inclusions in rock samples taken from boreholes. Data includes the borehole location, sample depth, stratigraphy, analytical methods and other relevant metadata, as well as the mass spectrometry results presented as atomic mass units (amu) from 2 to 180 in parts per million (ppm) electron volts.</div><div> Fluid inclusions (FI) are microscopic samples of fluids trapped within minerals in the rock matrix and cementation phases. Hence, these FIS data record the bulk volatile chemistry of the fluid inclusions (i.e., water, gas, and/or oil) present in the rock sample and determine the relative abundance of the trapped compounds (e.g., in amu order, hydrogen, helium, methane, ethane, carbon dioxide, higher molecular weight aliphatic and aromatic hydrocarbons, and heterocyclic compounds containing nitrogen, oxygen or sulfur). The FI composition can be used to identify the presence of organic- (i.e., biogenic or thermogenic) and inorganic-sourced gases. These data provide information about fluid preservation, migration pathways and are used to evaluate the potential for hydrocarbon (i.e. dry gas, wet gas, oil) and non-hydrocarbon (e.g., hydrogen, helium) resources in a basin. These data are collated from Geoscience Australia records, destructive analysis reports (DARs) and well completion reports (WCRs), with the results being delivered in the Fluid Inclusion Stratigraphy (FIS) web services on the Geoscience Australia Data Discovery Portal at https://portal.ga.gov.au which will be periodically updated.</div>

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 centred logratio 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 in 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. Inclusions of PCs in provenancing adds marginally to the performance. Maximising 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. <b>Citation:</b> de Caritat, P., Woods, B., Simpson, T., Nichols, C., Hoogenboom, L., Ilheo, A., Aberle, M.G. and Hoogewerff, J. (2021), Forensic soil provenancing in an urban/suburban setting: A sequential multivariate approach. <i>J Forensic Sci</i>, 66: 1679-1696. https://doi.org/10.1111/1556-4029.14727

<div>A novel method of estimating the silica (SiO2) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to calculate first the minimum SiO2, water (H2O) and carbon dioxide (CO2) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO2. In a recursive step, a ‘consensus’ SiO2 is then established as the average between the two aforementioned estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Ti elemental concentrations + ‘consensus’ SiO2 + reported trace element concentrations converted to wt% + ‘normative’ H2O + ‘normative’ CO2) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H2O and CO2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO2 is linear, strong (R2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO2 concentrations with another publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO2 values measured on the ground over the same study area. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to validate results.</div><div><br></div><div>The major oxide concentrations, including those for the estimated SiO2 and LOI, for the NASGL A and C horizons are available for download, as CSV files. A worked example for five selected NASGL C horizon samples is also available for download, as an XLSX file.</div> <b>Citation:</b> P. de Caritat, E. Grunsky, D.B. Smith; Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach. <i>Geochemistry: Exploration, Environment, Analysis</i> 2023; 23 (3): 2023-039 doi: https://doi.org/10.1144/geochem2023-039 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)