Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data

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.

3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning

“…As recently shown by von Hebel et al. (2019), an enhanced processing chain can provide accurate and quantitative EMI data. This offers interesting possibilities to extend the presented approach by depth‐true electrical conductivity values.…”

“…The penetration depth and footprint of the sensor data increase with increasing intercoil spacing. Vertically oriented magnetic dipoles (VDP/coil axis horizontal coplanar [HCP]) provide a higher depth penetration than horizontally oriented magnetic dipoles (HDP/coil axis vertical coplanar [VCP]) while taking into account the different cumulative sensitivity functions of both orientations (Callegary, Ferre, & Groom, 2012; Martini et al., 2017; McNeill, 1980b; von Hebel et al., 2019). The CMD‐Explorer and the CMD‐Mini‐Explorer enable simultaneous multi‐depth exploration of EC a with either VDP or HDP.…”

“…Before interpolation these quality control lines, as well as outliers related to a gas pipe line, were removed (Figure 1). Within the dataset, negative values of EC a occurred due to the custom calibration of the instrument (von Hebel et al., 2019). We corrected the measurements with an offset of 3.44 mS m −1 (CMD‐Mini‐Explorer VDP 0.32 m), 4.86 mS m −1 (CMD‐Mini‐Explorer HDP 0.32 m), and 0.21 mS m −1 (CMD‐Mini‐Explorer HDP 0.71 cm) to avoid confusion with these values and to make use of the containing information on spatial variability.…”

Simultaneous multichannel multi‐offset ground‐penetrating radar measurements for soil characterization

“…(b) Permittivity profile vs. depth obtained from interval velocities with groundwave permittivity on top. (c) For comparison, the depth of the ground-penetrating radar (GPR) layers (black) over the inverted electrical conductivity measured with electromagnetic induction (EMI) according to von Hebel et al (2014) (von Hebel et al, 2019). For a better comparison of both methods, the layer depths obtained by SiMoc GPR (averaged by 5 m) are plotted over the EMI inversion results (Figure 11c).…”

“…These results show that SiMoc GPR can be used to characterize the soil at different depths, especially in combination with EMI. Moreover, the inversion of accurate and quantitative EMI data (von Hebel et al., 2019) may be improved when guiding the layer boundaries in the EMI inversion process toward the depths inferred by GPR.…”