Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3421–3435, 2021
Geosci. Model Dev., 14, 3421–3435, 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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Cited articles

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Alley, N., Ckarjet, T., Macphail, M., and Truswell, E.: Sedimentary infillings and development of major Tertiary palaeodrainage systems of south-central Australia, in: Palaeoweathering, palaeosurfaces and related continental deposits, John Wiley and Sons, Hoboken, US, 73, 337, 2009. 
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Davis, A., Macaulay, S., Munday, T., Sorensen, C., Shudra, J., and Ibrahimi, T.: Uncovering the groundwater resource potential of Murchison Region in Western Australia through targeted application of airborne electromagnetics, ASEG Extended Abstracts, 2016, 1–6, 2016. 
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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.