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

Supplement

https://doi.org/10.5194/gmd-17-8535-2024-supplement

Data sets

Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.10) Artificial Neural Networks Ryan Love et al. https://zenodo.org/records/10042047

Model code and software

Supplemental Materials for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.10) Artificial Neural Networks Ryan Love et al. https://zenodo.org/records/10045463

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