Articles | Volume 6, issue 2
https://doi.org/10.5194/gchron-6-227-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gchron-6-227-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jun 2024
Research article |
| 12 Jun 2024
| 12 Jun 2024
Solving crustal heat transfer for thermochronology using physics-informed neural networks
School of Earth and Ocean Sciences, University of Victoria, Victoria, Canada
Shengze Cai
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China
Jean Braun
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
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Short summary
We demonstrate a machine learning method to estimate the temperature changes in the Earth's crust over time. The method respects physical laws and conditions imposed by users. By using observed rock cooling ages as constraints, the method can be used to estimate the tectonic and landscape evolution of the Earth. We show the applications of the method using a synthetic rock uplift model in 1D and an evolution model of a real mountain range in 3D.
We demonstrate a machine learning method to estimate the temperature changes in the Earth's...
