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

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

doi:https://doi.org/10.5194/hess-26-55-2022

Articles | Volume 26, issue 1

https://doi.org/10.5194/hess-26-55-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-55-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 1

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

Model code and software

Kimmvv/EMI-Maxwell: EMI K. M. van't Veen https://zenodo.org/record/5745749

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.