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Geochemical data are compositional in nature and are subject to the problemstypically associated with data that are restricted to the real non-negativenumber space with constant-sum constraint, that is, the simplex. Geochemistrycan be considered a proxy for mineralogy, comprised of atomically orderedstructures that define the placement and abundance of elements in the minerallattice structure. Based on the innovative contributions of John Aitchison, whointroduced the logratio transformation into compositional data analysis, thiscontribution provides a systematic workflow for assessing geochemical data in asimple and efficient way, such that significant geochemical (mineralogical)processes can be recognized and validated. This workflow, called GeoCoDA andpresented here in the form of a tutorial, enables the recognition of processesfrom which models can be constructed based on the associations of elements thatreflect mineralogy. Both the original compositional values and theirtransformation to logratios are considered. These models can reflectrock-forming processes, metamorphism, alteration and ore mineralization.Moreover, machine learning methods, both unsupervised and supervised, appliedto an optimized set of subcompositions of the data, provide a systematic,accurate, efficient and defensible approach to geochemical data analysis. Theworkflow is illustrated on lithogeochemical data from exploration of the Starkimberlite, consisting of a series of eruptions with five recognized phases.

GeoCoDA: Recognizing and Validating Structural Processes in Geochemical Data. A Workflow on Compositional Data Analysis in Lithogeochemistry