EGU23-4648, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

The emergence of Continental Crust Revealed by Machine Learning

Chuntao Liu1,2, C. Brenhin Keller3, Xiaoming Liu4, and Zhou Zhang1,2
Chuntao Liu et al.
  • 1School of Earth Sciences, Zhejiang University, Hangzhou, China (,
  • 2Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, Zhejiang University, Hangzhou, China (,
  • 3Department of Earth Sciences, Dartmouth College, Hanover, USA (
  • 4Department of Geological Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, USA (

Reconstructing the emergence of the modern continental crust is crucial to understand the evolution of the crust, the onset of plate tectonics, elemental cycling, and long-term climate. However, it remains highly contentious about when and how the major subaerial continental crust emerged over time. Here, we used a machine learning (ML) model (XGBoost) to reveal the exposed history of continental crust over the last 3.8 billion years ago (Ga). First, we compiled ~10,000 modern subaerial or submarine basalts with major and trace elements to train the ML model. Then, the trained ML model (with resampling) was utilized to predict and calculate the mean proportions of subaerially erupted continental basaltic rocks since 3.8 Ga. The result suggested that the subaerial proportion only reached about 50% at ~2.5 Ga, indicating the exposure of the continental crust was far from the present-day level at the end of the Archean era. On the other hand, since ~1.8 Ga, the subaerial proportion of the continental crust exhibited a dynamic balance between ~60% and 80%, reaching the present-day level.

How to cite: Liu, C., Keller, C. B., Liu, X., and Zhang, Z.: The emergence of Continental Crust Revealed by Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4648,, 2023.