Quantitative Provenance Analysis of the Yangtze and Yellow River Sediments Through Detrital Zircon U‐Pb Geochronology Using an XGBoost Machine Learning Algorithm

Abstract Over the past two decades, a large number of zircon U‐Pb ages from the Yangtze and Yellow River Basins have been published, yet distinguishing the sources of sediment between these regions remains challenging. Issues related to sampling, analytical methods, and biases complicate the interpretation of detrital zircon geochronology. In this study, we leveraged machine learning techniques to analyze a data set of over 33,000 zircon U‐Pb ages, refining the data to 28,082 ages for our analysis. We employed two characterization strategies: tectonic classification and kernel density estimation, and optimized our models through hyperparameter tuning. Our results demonstrated that the machine learning algorithm, eXtreme Gradient Boosting (XGBoost), significantly improved the accuracy of predicting sediment sources when compared to conventional methods (e.g., multidimensional scaling diagram). Additionally, we found that the most informative age populations were associated with the orogenic events (e.g., Jinning, 800–1,000 Ma, Tianshan, 260–394 Ma, and Nanhua, 680–800 Ma) rather than the movements of Lvliang (1,800–2,500 Ma) and Wutai (2,500–2,800 Ma), as suggested in previous studies. Finally, we tested the optimized models on several case studies, illustrating the effectiveness in identifying provenance signals for modern and quaternary sediments in East China Seas and the Yangtze Delta. While this machine learning approach shows great potential for improving sediment provenance analysis in these case studies, it is still limited by the availability and quality of detrital zircon age data for more detailed provenance analysis on sub‐basin scales.