| Abstract: | Geochemical exploration in secondary environments can be viewed as a particular manifestation of indirect geological observation. Geochemical anomalies in complex sample media reflect dispersion signatures, generally much disguised by secondary or higher-order mechanical and physico-chemical processes such as mixing, comminution, dilution, (re)transportation, weathering etc. Such complexities often make a thorough understanding of the origin of any particular sample type difficult ot obtain. The objective of data analysis in this context is to convert the geochemical data into a meaningful “signal”, particularly useful for prospecting, and other, in this case irrelevant, variability or “noise”. The experience of the last decades of practical exploration has clearly shown that statistical as well as geographical geochemical anomaly patterns are multi-element signatures. Using suitable multivariate statistical procedures (in the present case principal components modelling), it is possible to simultaneously define both a background data model and to quantify multivariate geochemical anomalies. This type of data analysis is guided very strongly by geological interaction, in which the emphasis is on modelling the background population(s), coupled with geographic plotting facilities. This outlier-screening facility is critical for many types of geochemical data evaluation. An example of this approach is described below. Another application of indirect multivariate data analysis is represented by PLS (Partial Least Squares) regression, which is a supervised pattern recognition and regression technique. We use it here to predict modal scheelite occurrences from regional stream-sediment data. |
