| Autor: |
Jiao, Ruohong, Cai, Shengze, Braun, Jean |
| Předmět: |
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| Zdroj: |
Geochronology; 10/19/2023, p1-25, 25p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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