Deciphering Earth’s deep mantle hemispheric geochemical dichotomy with machine learning

Abstract Global geochemical zoning of the mantle provides critical constraints on Earth’s internal dynamics and evolutionary history. However, whether geochemical heterogeneities in the deep mantle are dominated by the hemispheric DUPAL anomaly1,2 or by the two large low shear-wave velocity provinces (LLSVPs) has recently been debated3. Here, we employ machine learning to objectively assess the credibility of the two hypotheses on two different datasets of radiogenic isotopic records from global ocean island basalts. We observe discrepant classification accuracies for the LLSVP-based dichotomy and contradictory roles of the most characteristic 87Sr/86Sr isotopic ratio in two different datasets where the hemispheric DUPAL dichotomy remains robust and consistent. The two most important isotopic ratios, i.e., 87Sr/86Sr and 206Pb/204Pb, effectively distinguish the austral and boreal domains to the same extent as all the isotopic ratios combined. This discovery concisely defines the DUPAL anomaly in the 87Sr/86Sr - 206Pb/204Pb diagram, which highlights the key role of the Enriched Mantle 1 (EM1) component. The importance of EM1 supports the historical large-scale mass transfer of lower continental crust into the deep mantle in the Southern Hemisphere and could be attributed to widespread lithospheric delamination caused by continental collisions during Gondwana amalgamation at ~600-500 Ma. These observations illustrate how machine learning from large geochemical datasets contributes to revealing robust patterns in heterogeneous and evolutionarily deep Earth.