Quantifying Continental Crust Thickness Using the Machine Learning Method

Abstract

AbstractCrustal thickness plays a key role in many geological processes. However, it remains challenging to quantify crustal thickness in the geological past. Here we propose an Extremely Randomized Trees algorithm‐based machine learning model to recover crustal thickness of old geological regions. The model is trained using major oxide and trace element compositions of 1,480 young intermediate to felsic rocks from global arcs and collisional orogens and geophysical measurements of crustal thickness. The model provides better estimations of crustal thickness than the commonly used methods based on Sr/Y and (La/Yb)N when applied to the testing data. The validity of this model is further demonstrated by its applications to the Kohistan–Ladakh, Gangdese and Talkeetna arcs, where paleocrustal thicknesses have been well constrained. We then use this model to construct the Mesozoic crustal thickness evolution of the Erguna Block in the southeast of the Mongol–Okhotsk suture belt. The closure time of the suture zone is still debated. Our results suggest that the crustal thickness of the Erguna Block increased from 43 ± 9 km at 210 Ma to 62 ± 7 km at 180 Ma, remained constant between 180 and 150 Ma, and then thinned to 36 ± 4 km at 120 Ma. These results suggest that the Mongol–Okhotsk Ocean closed in the Early–Middle Jurassic and the thickened crust was stretched during the Cretaceous. We show that the thick crust and compression‐extension transition seem to be favorable for the formation of porphyry copper deposits in the Erguna Block during the Late Jurassic.