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Reconstructing Earth's atmospheric oxygenation history using machine learning.

Guoxiong Chen1, Qiuming Cheng2,3,4, Timothy W Lyons5

  • 1State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China.

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Machine learning reconstructs atmospheric oxygen (O2) levels over 4 billion years using igneous geochemistry. This reveals detailed O2 fluctuations, suggesting mantle cooling influenced Earth's oxygenation.

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Area of Science:

  • Geochemistry
  • Paleoclimatology
  • Machine Learning

Background:

  • Reconstructing historical atmospheric oxygen (O2) levels is crucial for understanding life's evolution.
  • Traditional sediment-based geochemical proxies have limitations due to data gaps.

Purpose of the Study:

  • To develop an independent method for reconstructing atmospheric O2 levels with higher temporal resolution.
  • To explore Earth's oxygenation history over the last 4.0 billion years.

Main Methods:

  • Utilized machine learning algorithms applied to a large dataset of global mafic igneous geochemistry.
  • Compared results with existing sediment-hosted paleo-oxybarometer data.

Main Results:

  • Observed a two-step rise in atmospheric O2, consistent with previous studies.
  • Identified detailed, shorter-term O2 fluctuations not previously well-established.
  • Machine learning approach provided a more granular view of oxygenation history.

Conclusions:

  • Earth's oxygenated atmosphere is partly a result of mantle cooling.
  • Evolving mantle melts played a role in modulating early O2 sources and sinks.
  • Machine learning offers a powerful new tool for paleoclimate reconstructions.