Journal Article

FZJ-2019-05329

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data

von Hebel, C. (Corresponding author)FZJ* ; van der Kruk, J. (Corresponding author)FZJ* ; Huisman, J. A.FZJ* ; Mester, A.FZJ* ; Altdorff, D. ; Endres, A. L. ; Zimmermann, E.FZJ* ; Garre, S. ; Vereecken, H.FZJ*

2019
MDPI Basel

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Please use a persistent id in citations: http://hdl.handle.net/2128/23223  doi:10.3390/s19214753

Abstract: Multi-coil electromagnetic induction (EMI) systems induce magnetic fields below and above the subsurface. The resulting magnetic field is measured at multiple coils increasingly separated from the transmitter in a rigid boom. This field relates to the subsurface apparent electrical conductivity (σa), and σa represents an average value for the depth range investigated with a specific coil separation and orientation. Multi-coil EMI data can be inverted to obtain layered bulk electrical conductivity models. However, above-ground stationary influences alter the signal and the inversion results can be unreliable. This study proposes an improved data processing chain, including EMI data calibration, conversion, and inversion. For the calibration of σa, three direct current resistivity techniques are compared: Electrical resistivity tomography with Dipole-Dipole and Schlumberger electrode arrays and vertical electrical soundings. All three methods obtained robust calibration results. The Dipole-Dipole-based calibration proved stable upon testing on different soil types. To further improve accuracy, we propose a non-linear exact EMI conversion to convert the magnetic field to σa. The complete processing workflow provides accurate and quantitative EMI data and the inversions reliable estimates of the intrinsic electrical conductivities. This improves the ability to combine EMI with, e.g., remote sensing, and the use of EMI for monitoring purposes.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)
2. JARA - HPC (JARA-HPC)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)
2. Better predictions with environmental simulation models: optimally integrating new data sources (jicg41_20100501) (jicg41_20100501)

Appears in the scientific report 2019

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Ebsco Academic Search ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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