Articles | Volume 23, issue 6
https://doi.org/10.5194/hess-23-2561-2019
https://doi.org/10.5194/hess-23-2561-2019
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

Viewed

Total article views: 2,966 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,874 1,015 77 2,966 74 68
  • HTML: 1,874
  • PDF: 1,015
  • XML: 77
  • Total: 2,966
  • BibTeX: 74
  • EndNote: 68
Views and downloads (calculated since 21 Jan 2019)
Cumulative views and downloads (calculated since 21 Jan 2019)

Viewed (geographical distribution)

Total article views: 2,966 (including HTML, PDF, and XML) Thereof 2,506 with geography defined and 460 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Jul 2024
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.