From manganese mineral evolution history to atmospheric oxygen reconstructionOA
The evolutionary record of redox-sensitive manganese(Mn)minerals encodes critical information about Earth’s oxygenation history.By building a global Mn mineral dataset(144200 entries across 25 feature dimensions),we developed a URD(Unequal-size feature matrix,Recoupling relationship,and Disaccord labels)deep-learning model to reconstruct continuous atmospheric oxygen level(p O2)changes over 4.0 billion years.Our results provide robust mineralogical evidence linking the timing and tempo of oxygenation to planetary-scale tectonics and biosphere evolution.Specially,the reconstruction reveals two distinct oxygenation modes:a protracted and gradual increase during the Paleoproterozoic-Mesoproterozoic,reflected in the moderately progressive evolution of Mn mineral assemblages;and a more rapid rise preceding and following the Neoproterozoic,coincided with supercontinent breakup and convergence,respectively-a pattern potentially driven by tectonic modulation of Mn supply and demand.This study introduces a mineral-informatic framework for decoding complex,high-dimensional mineral records,offering a transformative approach for systematically interrogating Earth’s long-term evolution.
Yan Li;Ziyi Zhuang;Xinran Xu;Rongzhang Yin;Yanzhang Li;Yadong Wang;Chunjiang Li;Yong Lai;Yanan Zhang;Huan Ye;Zhaoyang Hu;Anhuai Lu;Robert M.Hazen;Xiangzhi Bai
SKLab-DeepMinE,MOEKLab-OBCE,School of Earth and Space Sciences,Peking University,Beijing 100871,China Beijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaBeijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaImage Processing Center,Beihang University,Beijing 102206,ChinaSKLab-DeepMinE,MOEKLab-OBCE,School of Earth and Space Sciences,Peking University,Beijing 100871,China Beijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaSKLab-DeepMinE,MOEKLab-OBCE,School of Earth and Space Sciences,Peking University,Beijing 100871,China Beijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaImage Processing Center,Beihang University,Beijing 102206,ChinaSKLab-DeepMinE,MOEKLab-OBCE,School of Earth and Space Sciences,Peking University,Beijing 100871,China Beijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaSKLab-DeepMinE,MOEKLab-OBCE,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaImage Processing Center,Beihang University,Beijing 102206,ChinaSKLab-DeepMinE,MOEKLab-OBCE,School of Earth and Space Sciences,Peking University,Beijing 100871,China Beijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaSKLab-DeepMinE,MOEKLab-OBCE,School of Earth and Space Sciences,Peking University,Beijing 100871,China Beijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaBeijing Key Laboratory of Mineral Environmental Function,School of Earth and Space Sciences,Peking University,Beijing 100871,ChinaEarth and Planets Laboratory,Carnegie Institution for Science,Washington,DC 20015,USAImage Processing Center,Beihang University,Beijing 102206,China State Key Laboratory of Virtual Reality Technology and Systems,Beihang University,Beijing 100191,China Advanced Innovation Center for Biomedical Engineering,Beihang University,Beijing 100083,China
天文与地球科学
mineral informaticsMn mineral datasetatmospheric oxygenation historyMn crystal chemistrydeep learning
《National Science Review》 2026 (11)
P.177-191,15
supported by the National Natural Science Foundation of China(42192502 and 42372049)the Chinese Academy of Geological Sciences Basal Research Fund(JKYDM2025110).
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