Compositional symmetry between Earth’s crustal building blocks

Abstract

doi: 10.7185/geochemlet.1612Arc magmatism drives the production of modern continental crust. However, the mode of crustal differentiation in the geologic past, particularly in the Archean, remains controversial. Herein I adopt a compositional approach to interrogate a global, igneous geochemical database (EarthChem Library) and document the evolving compositional history of basalt, andesite, and rhyolite, which represent the three main crustal building blocks. Basalt and andesite yield synced geochemical trends and progressively incompatible element-rich and compatible element-poor compositions that are consistent with extensive partial mantlemelting during the Archean. Post-Archean basaltic to andesitic rocks also tend to be more alkaline in character, which coupled with their high field-strength-and large-ion lithophile-element signature, points to the increased influence of Phanerozoic-style intra-plate magmatism on the global, rock record. Coeval rhyolitic rocks are depleted in these same elements, suggesting that post-Archean felsic magmas track the evolving compositions of their basaltic to andesitic source, which are, in turn, controlled by the partial melting trend. These complementary, or symmetric, geochemical trends between rock types shifted during the Proterozoic and heralded the onset of modern compositional relationships between crustal building blocks.

Compositional Data Analysis (CoDA) of Clinopyroxene From Abyssal Peridotites

“…The CLR is performed by normalizing (centering) the log-transformed parts for each sample by its geometric mean. The log-transformation transforms compositional data (relative values with a constant sum) into multi-dimensional real space (Aitchison, 1982;Lawley, 2016), whereas normalizing to the geometric mean ensures that higher concentration elements (e.g., Ti in clinopyroxene) are not over-emphasized in further statistical analysis. Given an element, x i , within a total number of elements, D, geometric mean, g(x), the ith centered log-transformed data point z i is calculated as follows:…”

“…Data analysis using statistical and machine learning methods has recently been applied to the Earth sciences, showing great promise in classifications of geochemical data (Itano et al., 2020; Iwamori et al., 2017; Jorgenson et al., 2022; Lawley, 2016; Lawley et al., 2022; Ueki et al., 2018; Yoshida et al., 2018). However, its application to ultramafic rocks is relatively rare (Lawley et al., 2018, 2020; Qin et al., 2022).…”