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Reconstructing Earth’s atmospheric oxygenation history using machine learning

Author

Listed:
  • Guoxiong Chen

    (China University of Geosciences)

  • Qiuming Cheng

    (China University of Geosciences
    China University of Geosciences
    Sun Yat-sen University)

  • Timothy W. Lyons

    (University of California)

  • Jun Shen

    (China University of Geosciences)

  • Frits Agterberg

    (Geological Survey of Canada)

  • Ning Huang

    (China University of Geosciences)

  • Molei Zhao

    (China University of Geosciences)

Abstract

Reconstructing historical atmospheric oxygen (O2) levels at finer temporal resolution is a top priority for exploring the evolution of life on Earth. This goal, however, is challenged by gaps in traditionally employed sediment-hosted geochemical proxy data. Here, we propose an independent strategy—machine learning with global mafic igneous geochemistry big data to explore atmospheric oxygenation over the last 4.0 billion years. We observe an overall two-step rise of atmospheric O2 similar to the published curves derived from independent sediment-hosted paleo-oxybarometers but with a more detailed fabric of O2 fluctuations superimposed. These additional, shorter-term fluctuations are also consistent with previous but less well-established suggestions of O2 variability. We conclude from this agreement that Earth’s oxygenated atmosphere may therefore be at least partly a natural consequence of mantle cooling and specifically that evolving mantle melts collectively have helped modulate the balance of early O2 sources and sinks.

Suggested Citation

  • Guoxiong Chen & Qiuming Cheng & Timothy W. Lyons & Jun Shen & Frits Agterberg & Ning Huang & Molei Zhao, 2022. "Reconstructing Earth’s atmospheric oxygenation history using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33388-5
    DOI: 10.1038/s41467-022-33388-5
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