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09:54
A Model to Simulate Clinically Relevant Hypoxia in Humans
Published on: December 22, 2016
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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.
Nature Communications
|October 4, 2022
Summary
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.
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.
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