Geochemical data are compositional in nature and are subject to the problems
typically associated with data that are restricted to the real non-negative
number space with constant-sum constraint, that is, the simplex. Geochemistry
can be considered a proxy for mineralogy, comprised of atomically ordered
structures that define the placement and abundance of elements in the mineral
lattice structure. Based on the innovative contributions of John Aitchison, who
introduced the logratio transformation into compositional data analysis, this
contribution provides a systematic workflow for assessing geochemical data in
an efficient way, such that significant geochemical (mineralogical) processes
can be recognized and validated. The results of a workflow, called GeoCoDA and
presented here in the form of a tutorial, enables the recognition of processes
from which models can be constructed based on the associations of elements that
reflect mineralogy. Both the original compositional values and their
transformation to logratios are considered. These models can reflect rock
forming processes, metamorphic, alteration and ore mineralization. Moreover,
machine learning methods, both unsupervised and supervised, applied to an
optimized set of subcompositions of the data, provide a systematic, accurate,
efficient and defensible approach to geochemical data analysis. The workflow is
illustrated on lithogeochemical data from exploration of the Star kimberlite,
consisting of a series of eruptions with five recognized phases.Comment: 38 pages, 18 figures (including Supplementary Material