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


Interactive discussion

Status: closed
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
ED: Publish as is (20 May 2019) by Nadia Ursino
AR by Zhenjiao Jiang on behalf of the Authors (21 May 2019)  Author's response    Manuscript
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.