Automated rare earth element data assessment in GeoArmadillo and application to the GEOROC and PetDB databases

The amount of published geochemical data is continuously increasing due to the availability of advanced and highly efficient analytical tools. These data are made accessible through curated, domain-specific and interconnected synthesis databases. Such large data compilations bear great potential for leading-edge research in geochemistry and the application of compositional data of geoscientific samples in neighbouring fields. However, using data compiled from literature over many years also introduces pitfalls and uncertainties for data analysis. While the authors of the original data usually know their significance and limitations, “external” data users often find it challenging to assess the reliability of data, inherent uncertainties and, possibly, analytical artefacts. The group of rare earth elements (REEs) is widely used in geochemical research as tracers of chemical transport and differentiation, as well as geochemical fingerprinting. Therefore, we developed a data assessment method that can automatically screen geochemical data for suspicious REE patterns and anomalies. Our REE data assessment is based on REEs' geochemical behaviour in natural processes. Specifically, we address the “smoothness” of normalised REE patterns, identify unexpected outliers in REE plots, and various types of scatter. We extensively tested our REE data assessment method on large datasets extracted from the GEOROC and PetDB geochemical databases. We identified distinct types of anomalous REE patterns that we relate to potential artefacts and data quality issues. • Method to assess rare earth element (REE) data and automatically identify suspicious data. • REE data assessment was tested on large (>100,000 samples) datasets from GEOROC and PetDB. • REE data assessment enhances the reliability and usability of geochemical data for broader scientific research.