Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3421-2021
https://doi.org/10.5194/gmd-14-3421-2021
Model description paper
 | 
08 Jun 2021
Model description paper |  | 08 Jun 2021

Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping

Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters

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Latest update: 18 May 2024
Short summary
Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.