Geostatistics for compositional data: An overview

Document Type

Journal Article


Springer Berlin Heidelberg


School of Science


Originally published as: Tolosana-Delgado, R., Mueller, U., & van den Boogaart, K. G. (2019). Geostatistics for compositional data: An overview. Mathematical Geosciences, 51(4), 485-526. Original publication available here


This paper presents an overview of results for the geostatistical analysis of collocated multivariate data sets, whose variables form a composition, where the components represent the relative importance of the parts forming a whole. Such data sets occur most often in mining, hydrogeochemistry and soil science, but the results gathered here are relevant for any regionalised compositional data set. The paper covers the basic definitions, the analysis of the spatial codependence between components, mapping methods of cokriging and cosimulation honoring compositional constraints, the role of pre- and post-transformations such as log-ratios or multivariate normal score transforms, and block-support upscaling. The main result is that multivariate geostatistical techniques can and should be performed on log-ratio scores, in which case the system data-variograms-cokriging/cosimulation is intrinsically consistent, delivering the same results regardless of which log-ratio transformation was used to represent them. Proofs of all statements are included in an appendix.