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)

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments, and regolith in general, are usually carried out by governments and mineral exploration companies at continental (0.5 – 50 million km2), regional (500 – 500,000 km2) and local (0.5 – 500 km2) scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. A geochemical database, with 50 or more elements determined to sufficiently low detection limits, represents 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 comprised of many thousands of samples requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. When assembling large datasets from various sources, care must be taken to understand the nature of the sample media, the methods of sample collection and preparation, the laboratory digestion procedures and the analytical instrumentation methods. Problems that are typically associated with the interpretation of multi-element geochemical data include closure, missing values, censoring, merging, levelling different datasets, and adequate spatial sample design. Of particular significance is the effect of stoichiometry within the logratio framework that has been developed to deal with compositional data. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyze and interpret the information inherent to 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 after compositionally appropriate data imputation and transformation. Classification and predictive procedures, at the continental, regional and camp scales, can be used to confirm lithological variability, hydrothermal alteration, and mineralization. Studies of multi-element 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 mineralisation from a regional survey in the southern Thomson Orogen of northern New South Wales and southern Queensland to the continental scale, reveals a number of potential regions with similar or even higher mineral potential throughout Australia. Abstract presented at Exploration ’17 Sixth Decennial International Conference on Mineral Exploration (https://www.mining.com/web/exploration-17-sixth-decennial-internatioal-conference-mineral-exploration/