Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 23, issue 6
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.
https://doi.org/10.5194/hess-23-2561-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 6
Research article
 | 
13 Jun 2019
Research article |  | 13 Jun 2019

High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data

Zhenjiao Jiang, Dirk Mallants, Luk Peeters, Lei Gao, Camilla Soerensen, and Gregoire Mariethoz

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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.
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