Deep learning multimodal methods for geophysical inversion : application to glacier ice thickness estimation

Author:

Lopez Uroz Lorenzo1ORCID,Benoit Alexandre1ORCID,Yan Yajing1ORCID,Lin-Kwong-Chon Christophe1ORCID,Giffard-Roisin Sophie2ORCID,Rabatel Antoine3ORCID

Affiliation:

1. Listic-Université Savoie Mont Blanc, FR

2. ISTerre-Université Grenoble Alpes, FR

3. IGE-Université Grenoble Alpes, FR

Funder

Agence Nationale de la Recherche

Publisher

ACM

Reference16 articles.

1. Neural Networks Applied to Estimating Subglacial Topography and Glacier Volume

2. A consensus estimate for the ice thickness distribution of all glaciers on Earth

3. Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

4. Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. 770–778. https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. 770–778. https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html

5. Inversion of a Stokes glacier flow model emulated by deep learning

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