  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 a
simple and efficient way, such that significant geochemical (mineralogical)
processes can be recognized and validated. This 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, metamorphism, 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.
