Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-55-2022
https://doi.org/10.5194/hess-26-55-2022
Research article
 | 
06 Jan 2022
Research article |  | 06 Jan 2022

Using machine learning to predict optimal electromagnetic induction instrument configurations for characterizing the shallow subsurface

Kim Madsen van't Veen, Ty Paul Andrew Ferré, Bo Vangsø Iversen, and Christen Duus Børgesen

Viewed

Total article views: 1,980 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,385 558 37 1,980 27 23
  • HTML: 1,385
  • PDF: 558
  • XML: 37
  • Total: 1,980
  • BibTeX: 27
  • EndNote: 23
Views and downloads (calculated since 06 May 2021)
Cumulative views and downloads (calculated since 06 May 2021)

Viewed (geographical distribution)

Total article views: 1,980 (including HTML, PDF, and XML) Thereof 1,834 with geography defined and 146 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Apr 2024
Download
Short summary
Geophysical instruments are often used in hydrological surveys. A geophysical model that couples electrical conductivity in the subsurface layers with measurements from an electromagnetic induction instrument was combined with a machine learning algorithm. The study reveals that this combination can estimate the identifiability of electrical conductivity in a layered soil and provide insight into the best way to configure the instrument for a specific field site.