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 anomaly^1,2 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 reveals 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.
 

机器学习,地球化学,地球动力学,冈瓦那