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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-201', Anonymous Referee #1, 29 Jun 2021
    • AC1: 'Reply on RC1', Kim Madsen, 16 Aug 2021
  • RC2: 'Comment on hess-2021-201', Anonymous Referee #2, 09 Jul 2021
    • AC2: 'Reply on RC2', Kim Madsen, 16 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (18 Aug 2021) by Gerrit H. de Rooij
AR by Kim Madsen on behalf of the Authors (06 Oct 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Oct 2021) by Gerrit H. de Rooij
RR by Anonymous Referee #2 (12 Nov 2021)
ED: Publish subject to minor revisions (review by editor) (15 Nov 2021) by Gerrit H. de Rooij
AR by Kim Madsen on behalf of the Authors (30 Nov 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (01 Dec 2021) by Gerrit H. de Rooij
AR by Kim Madsen on behalf of the Authors (02 Dec 2021)  Author's response   Manuscript 
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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.