Articles | Volume 23, issue 6
https://doi.org/10.5194/hess-23-2561-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-2561-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jun 2019
Research article |
| 13 Jun 2019
| 13 Jun 2019
High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data
Zhenjiao Jiang
CORRESPONDING AUTHOR
Key Laboratory of Groundwater Resources and Environment, Ministry of
Education, College of Environment and Resources, Jilin University,
Changchun, 130021, China
CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
Dirk Mallants
CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
Luk Peeters
CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia
CSIRO Land & Water, Locked Bag 2, Glen Osmond, SA 5064, Australia
Camilla Soerensen
CSIRO Mineral Resources, Locked Bag 2, Glen Osmond, SA 5064, Australia
Gregoire Mariethoz
University of Lausanne, Faculty of Geosciences and Environment,
Institute of Earth Surface Dynamics, Lausanne, Switzerland
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Short summary
Paleovalleys often form productive aquifers in the semiarid and arid areas. A methodology based on deep learning is introduced to automatically generate high-resolution 3-D paleovalley maps from low-resolution electrical conductivity data derived from airborne geophysical surveys. It is validated by borehole logs and the surface valley indices that the proposed method in this study provides an effective tool for regional-scale paleovalley mapping and groundwater exploration.
Paleovalleys often form productive aquifers in the semiarid and arid areas. A methodology based...
