Recognizing and Validating Structural Processes in Geochemical Data
- Autores: Eric C. Grunsky, B.A. Kjarsgaard
- Localización: Proceedings of the 6th International Workshop on Compositional Data Analysis: Girona, 1-7 de juny de 2015 / coord. por Santiago Thió Fernández de Henestrosa, Josep Antoni Martín Fernández, 2015, ISBN 978-84-8458-451-3
- Idioma: inglés
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Resumen
Geochemical data are compositional in nature and are subject to the problems typically associated with data that is restricted in real positive numbers 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 with a mineral lattice structure. The arrangement of elements within one or more minerals that comprise rocks, soils and aqueous solutions define a linear model in terms of their geochemical expression. When methods such as principal component analysis are applied to multi-element geochemical data, the dominant components generally reflects 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
