Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3421–3435, 2021
https://doi.org/10.5194/gmd-14-3421-2021
Geosci. Model Dev., 14, 3421–3435, 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 et al.

Data sets

Multi-resolution Valley Bottom Flatness (MrVBF) J. Gallant, T. Dowling, and J. Austin https://doi.org/10.4225/08/5701C885AB4FE

Musgrave Province Airborne Electromagnetic Conductivity Grids T. Munday https://doi.org/10.25919/5d0868d48591e

Model code and software

Sub3DNet: A deep learning model for regional-scale 3D subsurface structure mapping Zhenjiao Jiang https://doi.org/10.7910/DVN/DDEIUV

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.