Deep learning multimodal methods for geophysical inversion : application to glacier ice thickness estimation
Author:
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
Link
https://dl.acm.org/doi/pdf/10.1145/3617233.3617267
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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