Abstract

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, interpreted from geophysical and geological data, reflect distinct tectonic domains comprised of early Archean to recent Cenozoic igneous, metamorphic and sedimentary rock assemblages. The geochemical data have been treated as compositional data to uniquely describe and characterize the geochemistry of the regolith overlying the major crustal blocks across Australia according to the following workflow: imputation of missing/censored data, log-ratio transformation, multivariate statistical analysis, multivariate geospatial (minimum/maximum autocorrelation factor) analysis, and classification. Using cross validation techniques, the uniqueness of each major crustal block has been quantified. The ability to predict the membership of a surface regolith sample to one or more of the major crustal blocks is demonstrated. The predicted crustal block assignments define spatially coherent regions that coincide with the known crustal blocks. In some areas, inaccurate predictions are due to uncertainty in the initial crustal boundary definition or from surficial processes that mask the crustal block geochemical signature. In conclusion, the geochemical composition of the Australian surface regolith generally can be used to map the underlying crustal architecture, despite secondary modifications due to physical transport and chemical weathering effects. This methodology is however less effective where extensive and thick sedimentary basins such as the Eromanga and Eucla basins overlie crustal blocks.

Highlights

  • Based on the previous work by Caritat and Grunsky (2013) and preliminary research for this paper, principal component analysis (PCA) biplots of the National Geochemical Survey of Australia (NGSA) data coded by major crustal blocks (MCBs) identifiers demonstrated similar linear trends and patterns that exhibit distinct differences and overlaps amongst the MCBs

  • maximum autocorrelaƟon factor (MAF) analysis includes the spatial context of the multi-element geochemistry, which will provide a spatially coherent pattern of trends associated with the MCBs, forming the basis for the following discussion on process validation

  • A critical step in process validation is the identification of variables that assist in uniquely defining the classes assigned to the data through the application of an ANOVA

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Summary

Introduction

The composition of surface sediment or soil (s) varies spatially as a result of changes in climate (cl), organisms (o), relief (r), parent material (p) and time (t). This was expressed over 75 years ago by Jenny (1941) in the well-known expression: s = f(cl, o, r, p, t). Drew et al (2010) and Grunsky et al (2013a, 2013b) showed that soil geochemistry over the United States of America could accurately map climate zones (cl), ecosystems (~o), landforms (~r), and surface geology (~p) at the continental scale All these variables are proxies for the soil formation controls listed above. Previous work by Caritat and Grunsky (2013) and Grunsky et al (2014) demonstrated the usefulness of applying multivariate statistical methods such as principal component analysis to describe linear combinations of elements that are controlled by the stoichiometry of minerals and associated geochemical/geological processes

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