Electromagnetic induction (EMI) systems measure the soil apparent electrical conductivity (ECa), which is related to the soil water content, texture, and salinity changes. Large‐scale EMI measurements often show relevant areal ECa patterns, but only few researchers have attempted to resolve vertical changes in electrical conductivity that in principle can be obtained using multiconfiguration EMI devices. In this work, we show that EMI measurements can be used to determine the lateral and vertical distribution of the electrical conductivity at the field scale and beyond. Processed ECa data for six coil configurations measured at the Selhausen (Germany) test site were calibrated using inverted electrical resistivity tomography (ERT) data from a short transect with a high ECa range, and regridded using a nearest neighbor interpolation. The quantitative ECa data at each grid node were inverted using a novel three‐layer inversion that uses the shuffled complex evolution (SCE) optimization and a Maxwell‐based electromagnetic forward model. The obtained 1‐D results were stitched together to form a 3‐D subsurface electrical conductivity model that showed smoothly varying electrical conductivities and layer thicknesses, indicating the stability of the inversion. The obtained electrical conductivity distributions were validated with low‐resolution grain size distribution maps and two 120 m long ERT transects that confirmed the obtained lateral and vertical large‐scale electrical conductivity patterns. Observed differences in the EMI and ERT inversion results were attributed to differences in soil water content between acquisition days. These findings indicate that EMI inversions can be used to infer hydrologically active layers.

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 (σ_a), and σ_a 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 σ_a, 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 σ_a. 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.

Electrical impedance tomography (EIT) is a promising method to characterize important hydrological properties of soil, sediments, and rocks. The characterization is based on the analysis of the phase response of the complex electrical conductivity in a broad frequency range (i.e. mHz to kHz). However, it is challenging to measure the small phase response of low-polarizable soils and rocks in the higher frequency range up to 10 kHz. In order to achieve the required phase accuracy in the kHz frequency range, an optimized measurement system and advanced model-based processing methods have been developed. Recently, EIT measurements at sites with low electrical conductivity have shown a new dominating phase error related to capacitive leakage currents between cable shields and soil. In order to correct this phase error, we developed an advanced finite element model that considers both leakage currents and capacitive coupling between the soil and the cable shields in the reconstruction of the complex electrical conductivity distribution. This advanced model also takes into account potential measurement errors due to high electrode impedances. The use of this advanced model reduced the new dominating error for media with low electrical conductivity. It was also found that the amount of leakage current is an additional indicator for data quality that can be used for data filtering. After application of a novel data filter based on the leakage current and the use of the advanced modelling approach, the phase error of the measured transfer impedances above 100 Hz was significantly reduced by a factor of 6 or more at 10 kHz. In addition, physically implausible positive phase values were effectively eliminated. The new correction method now enables the reconstruction of the complex electrical conductivity for frequencies up to 10 kHz at field sites with a low electrical conductivity.