Submitted as: model description paper 19 Jun 2020
Submitted as: model description paper | 19 Jun 2020
Surf3DNet1.0: A deep learning model for regional-scale 3D subsurface structure mapping
- 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, College of Environment and Resources, Jilin University, Changchun, 130021, China
- 2CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
- 3University of Lausanne, Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, Lausanne, Switzerland
- 4CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia
- 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, College of Environment and Resources, Jilin University, Changchun, 130021, China
- 2CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
- 3University of Lausanne, Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, Lausanne, Switzerland
- 4CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia
Abstract. This study introduces an efficient deep learning approach based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from surface observations. The method was applied to delineate palaeovalleys in an Australian desert landscape. The neural network was trained on a 6,400 km2 domain by using a land surface tomography as 2D input and an airborne electromagnetic (AEM)-derived probability map of palaeovalley presence as 3D output. The trained neural network has a maximum square error < 0.10 and produces a square error < 0.10 across 93 % of the validation areas, demonstrating that it is reliable in reconstructing 3D palaeovalley patterns beyond the training area. Due to its generic structure, the neural network structure designed in this study and the training algorithm have broad application potential to construct 3D geological features (ore bodies, aquifer) from 2D land surface observations.
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Zhenjiao Jiang et al.


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RC1: 'A comment on the performance of the Surf3DNet model', Anonymous Referee #1, 23 Nov 2020
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RC2: 'Review of "A deep learning model for regional-scale 3D subsurface structure mapping"', Anonymous Referee #2, 08 Dec 2020
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AC2: 'Interactive comment on “Sub3DNet1.0: A deep learning model for regional-scale 3D subsurface structure mapping” by Zhenjiao Jiang et al.', Zhenjiao Jiang, 14 Dec 2020
Zhenjiao Jiang et al.
Model code and software
A deep learning model for regional-scale 3D subsurface structure mapping J. et al. 2020 https://doi.org/10.7910/DVN/DDEIUV
Zhenjiao Jiang et al.
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