Classification of S- and I-type detrital zircon by machine learning and its application to supercontinental evolution

doi:10.7523/j.ucas.2023.065

Abstract

Abstract: Supercontinent evolution and distribution of detrital zircon with time is a long-term hot research topic. By using the stacking framework involving eight different machine learning methods and the area under curve (AUC) and accuracy proxy, a model is established to classify S- and I-type zircons. Applying the model to global detrital zircon dataset gives the distribution of S- and I-type zircon with time. After comparing the distribution with paleomagnetism and geological records, it is found that the S-type zircon distribution peak corresponds to the end of a supercontinent breakup and the start of assembly of the next supercontinent, and that the S-type zircon distribution valley (also the small peak of I-type zircon) is related to the maximum packing of a supercontinent and the start of its breakup. Based on the correlation of S-type zircon peak with global zircon big peak and the valley of S-type zircon with the global zircon small peak, it is proposed that big peaks of global zircon distribution with time represent a dispersive state of continents, during which magmatic activity is high producing both I- and S-type granites with also a high velocity of continent movement. By comparison, the small peaks in global zircon distribution represent a packing state of continents during which the supercontinent is stable with low magmatic activity producing mainly I-type granites and with a low velocity of continent movement. Finally, a high-accuracy decision function is provided to judge S- and I-type zircons and can be applied in related studies.

Key words: machine learning, S- and I-type detrital zircon, supercontinent cycle, stacking, primary component analysis

CLC Number:

P591+.1

SUN Zhihan, ZHANG Yigang. Classification of S- and I-type detrital zircon by machine learning and its application to supercontinental evolution[J]. Journal of University of Chinese Academy of Sciences, 2025, 42(2): 209-220.

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