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.

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Zhenjiao Jiang on behalf of the Authors (14 Dec 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (15 Dec 2020) by Andrew Wickert
ED: Reconsider after major revisions (07 Mar 2021) by Andrew Wickert
AR by Zhenjiao Jiang on behalf of the Authors (07 Apr 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (10 May 2021) by Andrew Wickert
AR by Zhenjiao Jiang on behalf of the Authors (12 May 2021)  Author's response    Manuscript
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.