Three‐dimensional imaging of subsurface structural patterns using quantitative large‐scale multiconfiguration electromagnetic induction data

Abstract

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.

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

“…For all the transects and all the depth layers considered, the parameters confirm the relatively loose relationship between σ b,EMI and σ b,TDR already observed in the graphs, both in terms of accuracy (the distance of the MRA line from the 1 : 1) and precision (the data scatter around the MRA line). Von Hebel et al (2014) found a similar behavior when comparing their EMI and ERT datasets. In that case, the EC a values measured by EMI systematically underestimated the EC a generated by applying EMI forward modeling to the σ b distribution retrieved by ERT.…”

“…Moreover, as discussed, for example, in Lavoué et al (2010), Mester et al (2011) andVon Hebel et al (2014), an instrumental shift in conductivity values could be observed due to system miscalibration and the influence of surrounding conditions such as temperature, solar radiation, power supply conditions, the presence of the operator, zero-leveling procedures, cables close to the system and/or the field setup (see, amongst others, Sudduth et al, 2001;Robinson et al, 2004;Abdu et al, 2007;Gebbers et al, 2009;Nüsch et al, 2010). Hence, the EC a data from EMI measurements would generally require a proper calibration.…”

“…To date, this issue has been tackled by applying two different strategies: the first is to use empirical calibration relations relating the depth-integrated EC a readings to the σ b values measured by alternative methods -like Time-domain reflectometry (TDR) -within discrete depth intervals (Rhoades and Corwin, 1981;Lesch et al, 1992;Triantafilis et al, 2000;Amezketa, 2006;Yao and Jingsong, 2010;Coppola et al, 2016). The second consists of the 1-D inversion of the observations from the EMI sensor to reconstruct the vertical conductivity profile (Borchers et al, 1997;Hendrickx et al, 2002;Monteiro Santos et al, 2010;Lavoué et al, 2010;Mester et al, 2011;Minsley et al, 2012;Deidda et al, 2014;Von Hebel et al, 2014).…”

“…Clearly, this strategy is extremely time-(and resource-) consuming. To avoid drilling, Lavoué et al (2010) introduced a calibration method, later also adopted by Mester et al (2011) and Von Hebel et al (2014), using the electrical conductivity distribution obtained from electrical resistivity tomography (ERT) data as input for electromagnetic forward modeling. The EC a values predicted on the basis of ERT data were used to remove the observed instrumental shift and correct the measured conductivity values by linear regression.…”

Methods to estimate changes in soil water for phenotyping root activity in the field

“…When s is increased the depth of soil contributing to the σ a measurement increases (e.g., McNeill 1980;Callegary et al 2007). Given a set σ a measurements obtained with different coil spacing and orientations, a 1-D vertical profile of soil conductivity can be estimated by inverse modelling (Mester et al 2011;von Hebel et al 2014).…”