Compositional data analysis in geochemistry: Are we sure to see what really occurs during natural processes?

Geochemical data are typically reported as compositions, in the form of some proportions such as weight percents, parts per million, etc., subject to a constant sum (e.g. 100%, 1,000,000 ppm). This latter implies that such data are “closed”; that is, for a composition of D-components, only D-1 components are required. The statistical analysis of compositional data has been a major issue for more than 100 years. The problem of spurious correlation, introduced by Karl Pearson in 1897, affects all data measuring parts of some whole, which are by definition, constrained; and such type of measurements are present in all fields of geochemical research. The use of the log-ratio transform was introduced by John Aitchison to overcome these constraints by opening the data into the real number space, within which standard statistical methods can be applied. However, many statisticians and users of statistics in the field of geochemistry are unaware of the problems affecting compositional data, as well as solutions that overcome these problems. A look into the ISI Web of Science and Scopus databases shows that most papers where compositional data are the core of a geochemical research continue to ignore methods to correctly manage constrained data. A key question is how we can demonstrate that the interpretation of the behaviour of chemical species in natural environment and in geochemical processes is improved when the compositional constraint of geochemical data is taken into account through the use of new methods. In order to achieve this aim, this special issue of the Journal of Geochemical Exploration focuses on the correct statistical analysis of compositional data. Applications in exploration, monitoring and environments by considering several geological matrices are presented and discussed illustrating that several paths can be followed to understand how geochemical processes work.