Davood Moghadas

Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography

Electromagnetic parameters of the subsurface such as electrical conductivity are of great interest for non-destructive determination of soil properties (e.g., clay content) or hydrologic state variables (e.g., soil water content). In the past decade, several non-invasive geophysical methods have been developed to measure subsurface parameters in situ . Among these methods, electromagnetic (EM) induction appears to be the most efficient one that is able to cover large areas in a short time. However, this method currently does not provide absolute values of electrical conductivity due to calibration problems, which hinders a quantitative analysis of the measurement. In this study, we propose to calibrate EM induction measurements with electrical conductivity values measured with electrical resistivity tomography (ERT). EM induction measures an apparent electrical conductivity at the surface, which represents a weighted average of the electrical conductivity distribution over a certain depth range, whereas ERT inversion can provide absolute values for local conductivities as a function of depth. EM induction and ERT measurements were collected along a 120-metre-long transect. To reconstruct the apparent electrical conductivity measured with EM induction, the inverted ERT data were used as input in an electromagnetic forward modelling tool for magnetic dipoles over a horizontally layered medium considering the frequencies and offsets used by the EM induction instruments. Comparison of the calculated and measured apparent electrical conductivities shows very similar trends but a shift in absolute values, which is attributed to system calibration problems. The observed shift can be corrected for by linear regression. This new calibration strategy for EM induction measurements now enables the quantitative mapping of electrical conductivity values over large areas.

Efficient loop antenna modeling for zero-offset, off-ground electromagnetic induction in multilayered media

Retrieval of the subsurface electrical properties from electromagnetic induction (EMI) data using inverse modeling relies in particular on the accuracy of the considered EMI model. We have developed a new EMI approach whereby a zero-offset, off-ground loop antenna is efficiently modeled using frequency-dependent, complex linear transfer functions and the air subsurface is described by a Green’s function for wave propagation in 3D multilayered media. To ensure proper calibration of the system, vector network analyzer (VNA) technology is used as the transmitter and receiver. An optimal integration path is proposed for fast evaluation of the spatial Green’s function from its spectral counterpart. We validated the antenna model in laboratory conditions with measurements performed with a loop antenna in free space and at different heights above a perfect electrical conductor. Provided that the loop antenna is high enough above the reflector (off-ground condition), the measured and modeled Green’s functions agr...

Estimation of soil salinity in a drip irrigation system by using joint inversion of multicoil electromagnetic induction measurements

Low frequency electromagnetic induction (EMI) is becoming a useful tool for soil characterization due to its fast measurement capability and sensitivity to soil moisture and salinity. In this research, a new EMI system (the CMD mini-Explorer) is used for subsurface characterization of soil salinity in a drip irrigation system via a joint inversion approach of multiconfiguration EMI measurements. EMI measurements were conducted across a farm where Acacia trees are irrigated with brackish water. In situ measurements of vertical bulk electrical conductivity (σb) were recorded in different pits along one of the transects to calibrate the EMI measurements and to compare with the modeled electrical conductivity (σ) obtained by the joint inversion of multiconfiguration EMI measurements. Estimates of σ were then converted into the universal standard of soil salinity measurement (i.e., electrical conductivity of a saturated soil paste extract – ECe). Soil apparent electrical conductivity (ECa) was repeatedly measured with the CMD mini-Explorer to investigate the temperature stability of the new system at a fixed location, where the ambient air temperature increased from 26°C to 46°C. Results indicate that the new EMI system is very stable in high temperature environments, especially above 40°C, where most other approaches give unstable measurements. In addition, the distribution pattern of soil salinity is well estimated quantitatively by the joint inversion of multicomponent EMI measurements. The approach of joint inversion of EMI measurements allows for the quantitative mapping of the soil salinity distribution pattern and can be utilized for the management of soil salinity.

1D joint multi‐offset inversion of time‐domain marine controlled source electromagnetic data

The accurate estimation of sub-seafloor resistivity features from marine controlled source electromagnetic data using inverse modelling is hindered due to the limitations of the inversion routines. The most commonly used one-dimensional inversion techniques for resolving subsurface resistivity structures are gradient-based methods, namely Occam and Marquardt. The first approach relies on the smoothness of the model and is recommended when there are no sharp resistivity boundaries. The Marquardt routine is relevant for many electromagnetic applications with sharp resistivity contrasts but subject to the appropriate choice of a starting model. In this paper, we explore the ability of different 1D inversion schemes to derive sub-seafloor resistivity structures from time domain marine controlled source electromagnetic data measured along an 8-km-long profile in the German North Sea. Seismic reflection data reveal a dipping shallow amplitude anomaly that was the target of the controleld source electromagnetic survey. We tested four inversion schemes to find suitable starting models for the final Marquardt inversion. In this respect, as a first scenario, Occam inversion results are considered a starting model for the subsequent Marquardt inversion (Occam–Marquardt). As a second scenario, we employ a global method called Differential Evolution Adaptive Metropolis and sequentially incorporate it with Marquardt inversion. The third approach corresponds to Marquardt inversion introducing lateral constraints. Finally, we include the lateral constraints in Differential Evolution Adaptive Metropolis optimization, and the results are sequentially utilized by Marquardt inversion. Occam–Marquardt may provide accurate estimation of the subsurface features, but it is dependent on the appropriate conversion of different multi-layered Occam model to an acceptable starting model for Marquardt inversion, which is not straightforward. Employing parameter spaces, the Differential Evolution Adaptive Metropolis approach can be pertinent to determine Marquardt a priori information; nevertheless, the uncertainties in Differential Evolution Adaptive Metropolis optimization will introduce some inaccuracies in Marquardt inversion results. Laterally constrained Marquardt may be promising to resolve sub-seafloor features, but it is not stable if there are significant lateral changes of the sub-seafloor structure due to the dependence of the method to the starting model. Including the lateral constraints in Differential Evolution Adaptive Metropolis approach allows for faster convergence of the routine with consistent results, furnishing more accurate estimation of a priori models for the subsequent Marquardt inversion.

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

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 fullwaveform 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.

A unified full-waveform method for modeling ground penetrating radar and electromagnetic induction data for non-destructive characterization of soil and materials

We propose a unified full-waveform method for modeling zero-offset, off-ground ground penetrating radar (GPR) and electromagnetic induction (EMI) in multilayered media. Both GPR and EMI systems are set up using vector network analyzer technology. The antennas are modeled using frequency dependent, complex transfer functions, which include interactions with the medium layers. Wave propagation and induction effects are accounted for by means of three-dimensional (3-D) Greens functions. Laboratory results demonstrated the high accuracy of the GPR and EMI models. This shows great promise for non-destructive characterization of soil and materials.

High-resolution imaging of a vineyard in south of France using ground penetrating radar and electromagnetic induction

GPR and EMI surveys were carried out in a vineyard in southern France in order to produce high-resolution maps of soil stratigraphy and to retrieve soil hydrogeophysical properties of the different soil layers. The preliminary results presented in this paper show large spatial variations of the vineyard soil properties, which are in accordance with the distribution of the different soil types within the study area. This is particularly observable from soil electrical conductivity data, which show strong spatial correlation with large areas of comparable values delimited by well-defined discontinuities, revealing sharp variations of soil characteristics over short distances. These discontinuities almost systematically correspond to the limits of the vineyard plots, though areas of contrasted soil electrical conductivity values are also found within some plots. Furthermore, the patterns of soil electrical conductivity are in good agreement with soil stratigraphy observed from GPR measurements. Finally, these results also highlighted compaction as a likely explanation to vine vigour problems observed locally in the vineyard. Future work will focus on the full-waveform inversion of GPR and EMI data to retrieve the properties of the different soil layers and to investigate the spatial variation of soil water availability in the study area, and provide on this basis recommendations for the vineyard management.

Data fusion of ground-penetrating radar and electromagnetic induction for reconstruction of soil electrical properties

We jointly analyzed the ground penetrating radar (GPR) and electromagnetic induction (EMI) synthetic data to reconstruct the electrical properties of multilayered media. The GPR and EMI systems operate in zero-offset, off-ground mode and are designed using vector network analyzer technology. We compared different approaches for GPR and EMI data fusion. As a first approach, we weighted the EMI and GPR data using the inverse of the data variance. The ideal point method was also employed as a second weighting scenario and the third approach is the naive Bayesian method. Synthetic GPR and EMI data was generated for the particular case of a two-layered medium. Analysis of the objective function response surfaces from the two first approaches demonstrated the benefit of combining the two sources of information. However, due to the variations of the GPR and EMI model sensitivities with respect to the medium electrical properties, the formulation of an optimal objective function based on the weighting methods is not straightforward. While the Bayesian method relies on assumptions with respect to the statistical distribution of the parameters, it may constitute a relevant alternative for GPR and EMI data fusion.

STDR: A Novel Approach for Enhancing and Edge Detection of Potential Field Data

Edge detection is one of the most important steps in the map interpretation of potential field data. In such a dataset, it is difficult to distinguish adjacent anomalous sources due to their field superposition. In particular, the presence of overlain shallow and deep magnetic/gravity sources leads to strong and weak anomalies. In this paper, we present an improved filter, STDR, which utilises the ratio of the second-order vertical derivative to the second-order total horizontal derivative at the tilt angle equation. The maximum and minimum values of this filter delineate the positive and negative anomalies, respectively. This novel filtering approach normalises the intensity of strong and weak anomalies, as well as anomalies with different depths and properties. Moreover, to better illustrate the edges, its total horizontal derivative (THD_STDR) is also used. For positive and negative anomalies, the maximum value of the THD_STDR filter shows the edges of the anomalies. The potentiality of the proposed method is examined through both synthetic and real case scenarios and the results are compared with a number of existing edge detector filters, namely TDR, THD_TDR, Theta and TDX. Due to substantial improvements in the filtering, STDR and its total horizontal derivative allow for more accurate estimation of anomaly edges in comparison with the other filtering techniques. As a consequence, the interpretation of the potential field data is more feasible using the STDR filtering method.

Application of Electromagnetic Induction to Monitor Changes in Soil Electrical Conductivity Profiles in Arid Agriculture

In this research, multi-configuration electromagnetic induction (EMI) measurements were conducted in a corn field to estimate variation in soil electrical conductivity profiles in the roots zone. Electromagnetic forward model based on the full solution of Maxwells equation was used to simulate the apparent electrical conductivity measured with EMI system (the CMD mini-Explorer). Joint inversion of multi-configuration EMI measurements were performed to estimate the vertical soil electrical conductivity profiles. The inversion minimizes the misfit between the measured and modeled soil apparent electrical conductivity by DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, which is based on Bayesain approach. Results indicate that soil electrical conductivity profiles have low values close to the corn plants, which indicates loss of soil moisture due to the root water uptake. These results offer valuable insights into future potential and emerging challenges in the development of joint analysis of multi-configuration EMI measurements to retrieve effective soil electrical conductivity profiles.