Articles | Volume 6, issue 2
https://doi.org/10.5194/gchron-6-227-2024
https://doi.org/10.5194/gchron-6-227-2024
Research article
 | 
12 Jun 2024
Research article |  | 12 Jun 2024

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

Ruohong Jiao, Shengze Cai, and Jean Braun

Related authors

Bias and error in modelling thermochronometric data: resolving a potential increase in Plio-Pleistocene erosion rate
Sean D. Willett, Frédéric Herman, Matthew Fox, Nadja Stalder, Todd A. Ehlers, Ruohong Jiao, and Rong Yang
Earth Surf. Dynam., 9, 1153–1221, https://doi.org/10.5194/esurf-9-1153-2021,https://doi.org/10.5194/esurf-9-1153-2021, 2021
Short summary

Related subject area

Geochronological data analysis/statistics/modelling
New controls on sedimentation and climate in the central equatorial Pacific Ocean
Allison W. Jacobel, Kassandra M. Costa, Lily M. Applebaum, and Serena Conde
Geochronology, 7, 123–138, https://doi.org/10.5194/gchron-7-123-2025,https://doi.org/10.5194/gchron-7-123-2025, 2025
Short summary
Measuring varve thickness using micro-computed tomography (µCT): a comparison with thin section
Marie-Eugénie Meusseunan Pascale Jamba, Pierre Francus, Antoine Gagnon-Poiré, and Guillaume St-Onge
Geochronology, 7, 83–111, https://doi.org/10.5194/gchron-7-83-2025,https://doi.org/10.5194/gchron-7-83-2025, 2025
Short summary
Controls on zircon age distributions in volcanic, porphyry and plutonic rocks
Chetan Nathwani, Dawid Szymanowski, Lorenzo Tavazzani, Sava Markovic, Adrianna L. Virmond, and Cyril Chelle-Michou
Geochronology, 7, 15–33, https://doi.org/10.5194/gchron-7-15-2025,https://doi.org/10.5194/gchron-7-15-2025, 2025
Short summary
Interpreting cooling dates and histories from laser ablation in situ (U–Th–Sm) ∕ He thermochronometry: a modelling perspective
Christoph Glotzbach and Todd A. Ehlers
Geochronology, 6, 697–717, https://doi.org/10.5194/gchron-6-697-2024,https://doi.org/10.5194/gchron-6-697-2024, 2024
Short summary
Short communication: Nanoscale heterogeneity of U and Pb in baddeleyite from atom probe tomography – 238U series alpha recoil effects and U atom clustering
Steven Denyszyn, Donald W. Davis, and Denis Fougerouse
Geochronology, 6, 607–619, https://doi.org/10.5194/gchron-6-607-2024,https://doi.org/10.5194/gchron-6-607-2024, 2024
Short summary

Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems, arXiv [preprint], https://doi.org/10.48550/arXiv.1603.04467, 16 March 2016. a, b
Boster, K. A., Cai, S., Ladrón-de Guevara, A., Sun, J., Zheng, X., Du, T., Thomas, J. H., Nedergaard, M., Karniadakis, G. E., and Kelley, D. H.: Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows, P. Natl. Acad. Sci. USA, 120, e2217744120, https://doi.org/10.1073/pnas.2217744120, 2023. a
Brandon, M. T., Roden-Tice, M. K., and Carver, J. I.: Late Cenozoic exhumation of the Cascadia accretionary wedge in the Olympic Mountains, northwest Washington State, Bull. Geol. Soc. Am., 110, 985–1009, https://doi.org/10.1130/0016-7606(1998)110<0985:LCEOTC>2.3.CO;2, 1998. a, b, c
Braun, J.: Pecube: a new finite-element code to solve the 3D heat transport equation including the effects of a time-varying, finite amplitude surface topography, Comput. Geosci., 29, 787–794, https://doi.org/10.1016/S0098-3004(03)00052-9, 2003. a, b, c, d, e
Braun, J. and Robert, X.: Constraints on the rate of post-orogenic erosional decay from low-temperature thermochronological data: Application to the Dabie Shan, China, Earth Surf. Proc. Land., 30, 1203–1225, https://doi.org/10.1002/esp.1271, 2005. a, b, c, d
Download
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.
Share