A systematic approach to the evaluation of geochemical data involves the use of multivariate methods that identify processes. These processes are represented by element associations that reflect mineralogy. Processes may be linear or nonlinear, depending on the type of process. Different metrics can reflect different processes. Metrics with coordinates derived from principal component analysis, independent component analysis, and t-distributed stochastic embedding, to name a few, reflect different processes. The dominant components of these metrics can be used to enhance the signal/noise ratio in the data. An integral part of process discovery is the geospatial coherence of multivariate signatures. Models can be constructed by tagging the dominant components with attributes such as geology or mineral deposit information. These models can be tested using a range of multivariate classification/validation/prediction procedures from which probability-based measures of likelihood can be determined and displayed geospatially. The application of these techniques requires acknowledgment of the limitations inherent in the data.
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