Compositional Data Analysis

Volume 187 of the series Springer Proceedings in Mathematics & Statistics pp 85-115


Recognizing and Validating Structural Processes in Geochemical Data: Examples from a Diamondiferous Kimberlite and a Regional Lake Sediment Geochemical Survey

  • E. C. GrunskyAffiliated withDepartment of Earth and Environmental Sciences, University of Waterloo Email author 
  • , B. A. KjarsgaardAffiliated withGeological Survey of Canada

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Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted in real positive number space, the simplex. Geochemistry is a proxy for mineralogy, and minerals are comprised of atomically ordered structures that define the placement and abundance of elements in the mineral lattice structure. The arrangement of elements within one or more minerals that comprise rocks, soils, and surficial sediments define a linear model in the Euclidean geometry of real space in terms of their geochemical expression. When methods such as principal component analysis are applied to multielement geochemical data, the dominant components generally reflect features related to mineralogy and describe geologic processes that are both independent and partially codependent. The dominant principal components can be used as a filter to eliminate noise or under-sampled processes in the data. These dominant components can be used to create predictive geological maps, or maps displaying recognizable geochemical processes. Using these techniques, we demonstrate that stoichiometrically controlled geochemical processes can be “discovered” and “validated” from two sets of data, one derived from drill-hole lithogeochemistry of a series of kimberlite eruptions and a second from a suite of granitic, metamorphic, volcanic, and sedimentary rocks.


Geochemistry Classification Lithologic prediction Compositional data analysis