Major oxides provide valuable information about the composition, origin, and properties of rocks and regolith. Analysing major oxides contributes significantly to understanding the nature of geological materials and processes (i.e. physical and chemical weathering) – with potential applications in resource exploration, engineering, environmental assessments, agriculture, and other fields. Traditionally most measurements of oxide concentrations are obtained by laboratory assay, often using X-ray fluorescence, on rock or regolith samples. To expand beyond the point measurements of the geochemical data, we have used a machine learning approach to produce seamless national scale grids for each of the major oxides. This approach builds predictive models by learning relationships between the site measurements of an oxide concentration (sourced from Geoscience Australia’s OZCHEM database and selected sites from state survey databases) and a comprehensive library of covariates (features). These covariates include: terrain derivatives; climate surfaces; geological maps; gamma-ray radiometric, magnetic, and gravity grids; and satellite imagery. This approach is used to derive national predictions for 10 major oxide concentrations at the resolution of the covariates (nominally 80 m). The models include the oxides of silicon (SiO2), aluminium (Al2O3), iron (Fe2O3tot), calcium (CaO), magnesium (MgO), manganese (MnO), potassium (K2O), sodium (Na2O), titanium (TiO2), and phosphorus (P2O5). The grids of oxide concentrations provided include the median of multiple models run as the prediction, and lower and upper (5th and 95th) percentiles as measures of the prediction’s uncertainty. Higher uncertainties correlate with greater spreads of model values. Differences in the features used in the model compared with the full feature space covering the entire continent are captured in the ‘covariate shift’ map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful, when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map. Details of the modelling approach, model uncertainties and datasets are describe in an attached word document “Model approach uncertainties”. This work is part of Geoscience Australia’s Exploring for the Future program that provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government. These data are published with the permission of the CEO, Geoscience Australia.

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments, and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets, and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyze and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration, and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc related Cu-Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. <b>Citation:</b> E. C. Grunsky, P. de Caritat; State-of-the-art analysis of geochemical data for mineral exploration. <i>Geochemistry: Exploration, Environment, Analysis</i> 2019; 20 (2): 217–232. doi: https://doi.org/10.1144/geochem2019-031 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)