Electromagnetic induction antenna modelling using a linear system of complex antenna transfer functions

Moghadas, Davood; André, Frédéric; Bradford, John H.; Kruk, Jan van der; Vereecken, Harry; Lambot, Sébastien

doi:10.3997/1873-0604.2012002

Cited by 21 publications

(23 citation statements)

References 36 publications

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“…[] and Moghadas et al . []. They recorded ECa at sampling locations and calibrated those against the theoretical EMI response calculated from the true electrical conductivity distribution measured at regular depth increments of particular soil cores.…”

Section: Introductionmentioning

confidence: 99%

“…One option to calibrate ECa measured with EMI uses soil cores as ground truth data. This approach was successfully applied by Triantafilis et al [2000] and Moghadas et al [2012]. They recorded ECa at sampling locations and calibrated those against the theoretical EMI response calculated from the true electrical conductivity distribution measured at regular depth increments of particular soil cores.…”

Section: Introductionmentioning

confidence: 99%

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

Hebel

Rudolph

Mester

et al. 2014

Water Resources Research

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121

102

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hebel

Rudolph

Mester

et al. 2014

Water Resources Research

Self Cite

121

102

View full text Add to dashboard Cite

show abstract

“…One option could be to use soil cores as ground-truth data. In this case, EC a measurements at the sampling locations can be compared against EC a data predicted by the theoretical forward response applied to the true electrical conductivity distribution measured directly on the soil cores (Triantafilis et al, 2000;Moghadas et al, 2012). Clearly, this strategy is extremely time-(and resource-) consuming.…”

mentioning

confidence: 99%

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Dragonetti

Comegna

Ajeel

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. This paper deals with the issue of monitoring the spatial distribution of bulk electrical conductivity, σ b , in the soil root zone by using electromagnetic induction (EMI) sensors under different water and salinity conditions. To deduce the actual distribution of depth-specific σ b from EMI apparent electrical conductivity (EC a ) measurements, we inverted the data by using a regularized 1-D inversion procedure designed to manage nonlinear multiple EMI-depth responses. The inversion technique is based on the coupling of the damped Gauss-Newton method with truncated generalized singular value decomposition (TGSVD). The illposedness of the EMI data inversion is addressed by using a sharp stabilizer term in the objective function. This specific stabilizer promotes the reconstruction of blocky targets, thereby contributing to enhance the spatial resolution of the EMI results in the presence of sharp boundaries (otherwise smeared out after the application of more standard Occamlike regularization strategies searching for smooth solutions). Time-domain reflectometry (TDR) data are used as groundtruth data for calibration of the inversion results. An experimental field was divided into four transects 30 m long and 2.8 m wide, cultivated with green bean, and irrigated with water at two different salinity levels and using two different irrigation volumes. Clearly, this induces different salinity and water contents within the soil profiles. For each transect, 26 regularly spaced monitoring soundings (1 m apart) were selected for the collection of (i) Geonics EM-38 and (ii) Tektronix reflectometer data. Despite the original discrepancies in the EMI and TDR data, we found a significant correlation of the means and standard deviations of the two data series; in particular, after a low-pass spatial filtering of the TDR data. Based on these findings, this paper introduces a novel methodology to calibrate EMI-based electrical conductivities via TDR direct measurements. This calibration strategy consists of a linear mapping of the original inversion results into a new conductivity spatial distribution with the coefficients of the transformation uniquely based on the statistics of the two original measurement datasets (EMI and TDR conductivities).

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“…The transformation back to the spatial domain is performed by evaluating numerically a semi‐infinite integral, for which a fast procedure is applied (Lambot et al ). As the validity of this model is independent of frequency, the approach has also been applied to electromagnetic induction (Moghadas et al , ). The characteristic antenna properties

H (ω), H_{i} (ω)

, and

H_{f} (ω)

can be estimated for the entire frequency range by solving equation () based on different configurations, which, in our study, are antenna heights above a copper plane as an infinite perfect electrical conductor (PEC).…”

Section: Theorymentioning

confidence: 99%

Estimation of the near surface soil water content during evaporation using air‐launched ground‐penetrating radar

Moghadas

Jadoon

Vanderborght

et al. 2013

Near Surface Geophysics

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Evaporation is an important process in the global water cycle and its variation affects the near surface soil water content, which is crucial for surface hydrology and climate modelling. Soil evaporation rate is often characterized by two distinct phases, namely, the energy limited phase (stage‐I) and the soil hydraulic limited period (stage‐II). In this paper, a laboratory experiment was conducted using a sand box filled with fine sand, which was subject to evaporation for a period of twenty three days. The setup was equipped with a weighting system to record automatically the weight of the sand box with a constant time‐step. Furthermore, time‐lapse air‐launched ground penetrating radar (GPR) measurements were performed to monitor the evaporation process. The GPR model involves a full‐waveform frequency‐domain solution of Maxwell’s equations for wave propagation in three‐dimensional multilayered media. The accuracy of the full‐waveform GPR forward modelling with respect to three different petrophysical models was investigated. Moreover, full‐waveform inversion of the GPR data was used to estimate the quantitative information, such as near surface soil water content. The two stages of evaporation can be clearly observed in the radargram, which indicates qualitatively that enough information is contained in the GPR data. The full‐waveform GPR inversion allows for accurate estimation of the near surface soil water content during extended evaporation phases, when a wide frequency range of GPR (0.8–5.0 GHz) is taken into account. In addition, the results indicate that the CRIM model may constitute a relevant alternative in solving the frequency‐dependency issue for full waveform GPR modelling.

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Electromagnetic induction antenna modelling using a linear system of complex antenna transfer functions

Cited by 21 publications

References 36 publications

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

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

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Estimation of the near surface soil water content during evaporation using air‐launched ground‐penetrating radar

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