Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt

Abstract

AbstractThe mass transfer history of rocks provides direct evidence for fluid–rock interaction within the lithosphere and is recorded by compositional changes, especially in trace elements. The general method adopted for mass transfer analysis is to compare the composition of the protolith/precursor with that of metamorphosed/altered rocks; however, in many cases the protolith cannot be sampled. With the aim of reconstructing the mass transfer history of metabasalt, this study developed protolith reconstruction models (PRMs) for metabasalt using machine-learning algorithms. We designed models to estimate basalt trace-element concentrations from the concentrations of a few (1–9) trace elements, trained with a compositional dataset for fresh basalts, including mid-ocean ridge, ocean-island, and volcanic arc basalts. The developed PRMs were able to estimate basalt trace-element compositions (e.g., Rb, Ba, U, K, Pb, Sr, and rare-earth elements) from only four input elements with a reproducibility of ~ 0.1 log10units (i.e., ± 25%). As a representative example, we present PRMs where the input elements are Th, Nb, Zr, and Ti, which are typically immobile during metamorphism. Case studies demonstrate the applicability of PRMs to seafloor altered basalt and metabasalt. This method enables us to analyze quantitative mass transfer in regional metamorphic rocks or alteration zones where the protolith is heterogeneous or unknown.