A Workflow for the Analysis of Multivariate Exploration Geochemical Data

Multi-element geochemical data derived from systematic surveys from a variety of media within a geospatial continuum contain information that provides insight into processes that reflect mineralogy, paragenesis, alteration and economic mineralization. The relationships of the elements and corresponding attributes using a range of data analytical methods, including univariate and multivariate statistical methods along with machine learning, provide the framework for building knowledge from which extended methods of artificial intelligence can enhance model building and mineral systems discovery. This contribution provides the elements for the design of workflows that meet the requirements of enhancing knowledge from geochemical and mineralogical surveys for the purposes of geologic mapping, mineral resource prediction, or environmental management. Further, it outlines a framework for a systematic evaluation of geochemical data plus attributes that enable the discovery of processes from which models can be constructed and tested using machine learning methods. Methods are described for “process discovery” followed by additional methods for “process validation/prediction”. Six case studies are presented that highlight different approaches to the discovery of processes that assist in mineral exploration and geologic mapping at various scales. The workflow includes caveats and flags potential issues or limitations on what can be discovered and validated.