| Summary: | The variation of crustal thickness is a critical index to reveal how the continental crust evolved over its four billion years. Generally, ratios of whole-rock trace elements, such as Sr/Y, (La/Yb)n and Ce/Y, are used to characterize crustal thicknesses. However, sometimes confusing results are obtained since there is no enough filtered data. Here, a state-of-the-art approach, based on a machine-learning algorithm, is proposed to predict crustal thickness using global major- and trace-element geochemical data of intermediate arc rocks and intraplate basalts, and their corresponding crustal thicknesses. After the validation processes, the root-mean-square error (RMSE) and the coefficient of determination (R2) score were used to evaluate the performance of the machine learning algorithm based on the learning dataset which has never been used during the training phase. The results demonstrate that the machine learning algorithm is more reliable in predicting crustal thickness than the conventional methods. The trained model predicts that the crustal thickness of the eastern North China Craton (ENCC) was ~45 km from the Late Triassic to the Early Cretaceous, but ~35 km from the Early Cretaceous, which corresponds to the paleo-elevation of 3.0 ± 1.5 km at Early Mesozoic, and decease to the present-day elevation in the ENCC. The estimates are generally consistent with the previous studies on xenoliths from the lower crust and on the paleoenvironment of the coastal mountain of the ENCC, which indicates that the lower crust of the ENCC was delaminated abruptly at the Early Cretaceous.
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