Geochemical data are often very noisy due to the large natural heterogeneity of the geological materials as well as oversimplification of rock classification. This has serious repercussions on the precision of our knowledge of the deep past as we often rely solely on geochemical proxies to investigate the geological evolution of Archean and Proterozoic environments. Here statistical learning procedures were applied to achieve unbiased classification of Neoarchean stromatolitic dolostone textures on the basis of the distribution of their trace elements and rare earth elements (REE) investigated through laser ablation induced coupled plasma – mass spectrometry (LA-ICP-MS). Multivariate statistical analyses and supervised statistical learning have revealed that different dolomite fabrics, thought as products of aggrading diagenesis and recrystallization, are in fact chemically indistinguishable. The diagenetic processes that cause the re-crystallization of dolomite and the consequent change of textures, is not affecting the distribution of major and trace elements inherited by the depositional environment or during early stages of diagenesis. At the same time the algorithm has revealed that an optically homogeneous microcrystalline dolomite sample may in fact be geochemically inhomogeneous because of processes of ripening and recrystallization occurred at an early stage of marine diagenesis and that have contributed to element mobilization. Statistical learning has succeeded in recognizing chemofacies which not always overlap with dolomite textures and fabrics highlighting the importance of crystallographic and diagenetic studies before any study of carbonates as geochemical proxies.
All Science Journal Classification (ASJC) codes
- Geochemistry and Petrology