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
Minimizing the effects of Pb loss in detrital and igneous U–Pb zircon geochronology by CA-LA-ICP-MS
Erin E. Donaghy, Michael P. Eddy, Federico Moreno, and Mauricio Ibañez-Mejia
Geochronology, 6, 89–106, https://doi.org/10.5194/gchron-6-89-2024,https://doi.org/10.5194/gchron-6-89-2024, 2024
Short summary
Technical note: RA138 Calcite U-Pb LA-ICP-MS primary reference material
Marcel Guillong, Elias Samankassou, Inigo A. Müller, Dawid Szymanowski, Nathan Looser, Lorenzo Tavazzani, Óscar Merino-Tomé, Juan R. Bahamonde, Yannick Buret, and Maria Ovtcharova
Geochronology Discuss., https://doi.org/10.5194/gchron-2024-7,https://doi.org/10.5194/gchron-2024-7, 2024
Revised manuscript accepted for GChron
Short summary
(anchored) isochrons in IsoplotR
Pieter Vermeesch
Geochronology Discuss., https://doi.org/10.5194/gchron-2024-5,https://doi.org/10.5194/gchron-2024-5, 2024
Revised manuscript accepted for GChron
Short summary
Modeling apparent Pb loss in zircon U–Pb geochronology
Glenn R. Sharman and Matthew A. Malkowski
Geochronology, 6, 37–51, https://doi.org/10.5194/gchron-6-37-2024,https://doi.org/10.5194/gchron-6-37-2024, 2024
Short summary
Calibration methods for laser ablation Rb–Sr geochronology: comparisons and recommendation based on NIST glass and natural reference materials
Stijn Glorie, Sarah E. Gilbert, Martin Hand, and Jarred C. Lloyd
Geochronology, 6, 21–36, https://doi.org/10.5194/gchron-6-21-2024,https://doi.org/10.5194/gchron-6-21-2024, 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.