Preprints
https://doi.org/10.5194/gchron-2023-24
https://doi.org/10.5194/gchron-2023-24
19 Oct 2023
 | 19 Oct 2023
Status: a revised version of this preprint was accepted for the journal GChron and is expected to appear here in due course.

Solving crustal heat transfer for thermochronology using physics-informed neural networks

Ruohong Jiao, Shengze Cai, and Jean Braun

Abstract. We present a deep learning approach based on the physics-informed neural networks (PINNs) for estimating thermal evolution of the crust during tectonic uplift with a changing landscape. The approach approximates the temperature field of the crust with a deep neural network, which is trained by optimizing the heat advection-diffusion equation under boundary conditions such as initial and final thermal structure, topographic history, and surface and basal temperatures. From the trained neural network of temperature field and the prescribed velocity field, one can predict the temperature history of a given rock particle that can be used to compute the cooling ages of thermochronology. For the inverse problem, the forward model can be combined with a global optimization algorithm that minimizes the misfit between predicted and observed thermochronological data, in order to constrain unknown parameters in the uplift history or boundary conditions. We demonstrate the approach with solutions of one- and three-dimensional forward and inverse models of the crustal thermal evolution, which are consistent with results of the finite-element method. The three-dimensional model simulates the post-orogenic topographic decay of the Dabie Shan, China, with constraints from fission-track and (U-Th)/He ages.

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Ruohong Jiao, Shengze Cai, and Jean Braun

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gchron-2023-24', Kendra Murray, 23 Nov 2023
    • AC2: 'Reply on RC1', Ruohong Jiao, 23 Jan 2024
  • RC2: 'Comment on gchron-2023-24', David Whipp, 18 Dec 2023
    • AC1: 'Reply on RC2', Ruohong Jiao, 23 Jan 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gchron-2023-24', Kendra Murray, 23 Nov 2023
    • AC2: 'Reply on RC1', Ruohong Jiao, 23 Jan 2024
  • RC2: 'Comment on gchron-2023-24', David Whipp, 18 Dec 2023
    • AC1: 'Reply on RC2', Ruohong Jiao, 23 Jan 2024
Ruohong Jiao, Shengze Cai, and Jean Braun

Model code and software

PINNs for thermochronology Ruohong Jiao https://github.com/jiaor/PINNs_Chron/

Ruohong Jiao, Shengze Cai, and Jean Braun

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Short summary
We demonstrate a machine learning method to estimate the temperature changes of 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.