Reconstructing Earth's atmospheric oxygenation history using machine learning.

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 O

SUBMITTER: Chen G 

PROVIDER: S-EPMC9532422 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature