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)

Vulnerability functions, that relate damage to hazard magnitude are used in risk and impact assessments, mitigation studies and associated cost benefit analyses. The development of vulnerability functions must address the variety of assets exposed to the hazard of interest and the common scarcity of empirical data to calibrate any functions developed using heuristic or analytical methods. This record reports efforts to improve the knowledge of the vulnerability of Australian domestic housing to riverine inundation. The research is focussed on housing types found in the south-east of Queensland although the results can be applied to houses of similar type elsewhere in Australia. In order to address the wide variety of housing types found in the Australian built environment, in this research representative generic housing types are identified from surveyed building exposure. Analytical vulnerability relationships are developed for each from assessments of repair works at different inundation depths. Finally, the analytical vulnerability curves are compared to empirical data derived from repair costs reported by postal surveys of dwellings affected by flooding in Brisbane-Ipswich, January 2011, and Bundaberg, January 2013. Analytical vulnerability curves are presented for twelve generic housing types and two insurance regimes. The process of developing vulnerability curves analytically is compared to empirical data. The empirical data shows that for insured houses, the analytically derived vulnerability curves provide a reasonable estimate of direct losses. However, for uninsured houses the analytical vulnerability curves are shown to overestimate direct losses. The difference may be due to uninsured residents tolerating a greater level of residual damage or undertaking repairs themselves at cheaper rates than those assumed for the analytical work. Although the results display variability, the empirical data indicate that the presented analytical methodology for constructing vulnerability curves yields reasonable curves that would be suitable for modelling impact of riverine flooding on populations of houses provided that adjustments are made to modelled losses for uninsured dwellings.