Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 23
Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8535-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8535-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 23
Development and technical paper
 | 
02 Dec 2024
Development and technical paper |  | 02 Dec 2024

A fast surrogate model for 3D Earth glacial isostatic adjustment using Tensorflow (v2.8.0) artificial neural networks

Ryan Love, Glenn A. Milne, Parviz Ajourlou, Soran Parang, Lev Tarasov, and Konstantin Latychev

Viewed

Total article views: 2,472 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,008 366 98 2,472 203 81 136
  • HTML: 2,008
  • PDF: 366
  • XML: 98
  • Total: 2,472
  • Supplement: 203
  • BibTeX: 81
  • EndNote: 136
Views and downloads (calculated since 02 Nov 2023)
Cumulative views and downloads (calculated since 02 Nov 2023)

Viewed (geographical distribution)

Total article views: 2,472 (including HTML, PDF, and XML) Thereof 2,440 with geography defined and 32 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Apr 2026
Download
Short summary
A relatively recent advance in glacial isostatic adjustment modeling has been the development of models that include 3D Earth structure, as opposed to 1D structure. However, a major limitation is the computational expense. We have developed a method using artificial neural networks to emulate the influence of 3D Earth models to affordably constrain the viscosity parameter space. Our results indicate that the misfits are of a scale such that useful predictions of relative sea level can be made.
Read more
Share