Dynamic evolution of changbaishan volcanism in Northeast China illuminated by machine learning

Previous studies propose that there is a mantle upwelling that generated the Cenozoic basalts in Changbaishan. However, the dominant source and mechanism of the mantle upwelling remains highly debated. Here we apply machine learning algorithms of Random Forest and Deep Neural Network to train models using global island arc and ocean island basalts data. The trained models predict that Changbaishan basalts are highly influenced by slab-derived fluid. More importantly, the fluid effect decreases with no (Sr-87/Sr-86)(0) and epsilon(Nd(t)) changes between 5 Ma and 1 Ma, then enhances with increasing epsilon(Nd(t)) and decreasing (Sr-87/Sr-86)(0) after 1 Ma. We propose that a gap opened at about 5 Ma and the hot sub-slab oceanic asthenosphere rose through the gap after 1 Ma, generating the basalts enriched in fluid mobile elements and with the addition of depleted mantle component derived from the sub-slab oceanic asthenosphere.