利用机器学习重建地球大气氧化历史
Reconstructing Earth's atmospheric oxygenation history using machine learning.
作者信息
Chen Guoxiong, Cheng Qiuming, Lyons Timothy W, Shen Jun, Agterberg Frits, Huang Ning, Zhao Molei
机构信息
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China.
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, 10083, China.
出版信息
Nat Commun. 2022 Oct 4;13(1):5862. doi: 10.1038/s41467-022-33388-5.
Reconstructing historical atmospheric oxygen (O) 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 O similar to the published curves derived from independent sediment-hosted paleo-oxybarometers but with a more detailed fabric of O fluctuations superimposed. These additional, shorter-term fluctuations are also consistent with previous but less well-established suggestions of O 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 O sources and sinks.
以更高的时间分辨率重建历史大气氧(O)水平是探索地球生命演化的首要任务。然而,这一目标受到传统上使用的沉积物宿主地球化学代理数据缺口的挑战。在此,我们提出一种独立策略——利用全球镁铁质火成岩地球化学大数据进行机器学习,以探索过去40亿年的大气氧化作用。我们观察到大气O总体上呈两步上升,类似于从独立的沉积物宿主古氧压计得出的已发表曲线,但叠加了更详细的O波动结构。这些额外的、短期的波动也与先前关于O变异性的建议一致,但此前这些建议的确定性较低。我们从这一一致性得出结论,因此地球的氧化大气可能至少部分是地幔冷却的自然结果,具体而言,不断演化的地幔熔体共同帮助调节了早期O源和汇的平衡。