MULTIDIMENSIONAL SCALING ANALYSIS OF DETRITAL AGE DISTRIBUTIONS AND COMPOSITIONS
Principal Components Analysis (PCA) is a proven method that has been widely used in the context of compositional data analysis and traditional heavy mineral studies. Unfortunately, PCA cannot be readily applied to geochronological data, which are rapidly overtaking petrographic techniques as the method of choice for large scale provenance studies. Multidimensional Scaling (MDS) is a standard statistical tool ideally suited to fill this void.
MDS is a robust and flexible superset of PCA which makes fewer assumptions about the data. Given a table of pairwise ‘dissimilarities’ between samples, MDS produces a ‘map’ of points on which ‘similar’ samples cluster closely together, and ‘dissimilar’ samples plot far apart. The statistical effect size of the Kolmogorov-Smirnov test is a viable dissimilarity measure. This is not the case for the p-values of this and other tests.
A number of case studies in southern Africa, China and elsewhere convincingly show that MDS can effectively unravel subtle but meaningful variations in the detrital age spectra and heavy mineral compositions of silicliclastic sediments. Thus, MDS represents a simple yet powerful new tool to distill geologically meaningful information from large, complex datasets of multivariate data.
Further information and supporting software can be found on http://mudisc.london-geochron.com.