Regularized Inversion of Multi-Frequency EM Data in Geophysical Applications

Alba, Patricia Díaz de; Rodriguez, Giuseppe

doi:10.1007/978-3-319-32013-7_20

Cited by 12 publications

(13 citation statements)

References 7 publications

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“…Inverting for the magnetic permeability is sometimes not only necessary but it can also provide an additional tool for soil characterization (e.g., Beard and Nyquist, 1998;Farquharson et al, 2003;Sasaki et al, 2010;Guillemoteau et al, 2016;Noh et al, 2017;Deidda et al, 2017).…”

Section: Multi-height Emi Readings Inversionmentioning

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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Section: Multi-height Emi Readings Inversionmentioning

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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“…Advanced inversion techniques hold the potential of being applied effectively on small-scale EMI data, such as described above, e.g., [89,90]. While this is not yet common practice for soil moisture monitoring, some notable applications already exist, e.g., [91].…”

Section: Electromagnetic Induction (Emi)mentioning

confidence: 99%

Local- and Plot-Scale Measurements of Soil Moisture: Time and Spatially Resolved Field Techniques in Plain, Hill and Mountain Sites

Raffelli

Previati

Canone

et al. 2017

Water

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Soil moisture measurement is essential to validate hydrological models and satellite data. In this work we provide an overview of different local and plot scale soil moisture measurement techniques applied in three different conditions in terms of altitude, land use, and soil type, namely a plain, a mountain meadow and a hilly vineyard. The main goal is to provide a synoptic view of techniques supported by practical case studies to show that in such different conditions it is possible to estimate a time and spatially resolved soil moisture by the same combination of instruments: contact-based methods (i.e., Time Domain Reflectometry-TDR, and two low frequency probes) for the time resolved, and hydro-geophysical minimally-invasive methods (i.e., Electromagnetic Induction-EMI, Ground Penetrating Radar-GPR, and the Electrical Resistivity Tomography-ERT) for the spatially resolved. Both long-term soil moisture measurements and spatially resolved measurement campaigns are discussed. Technical and operational measures are detailed to allow critical factors to be identified.

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“…The real part (in-phase component) is affected mainly by the magnetic permeability of the soil, while the imaginary part (out-of-phase or quadrature component) is affected mainly by its electrical conductivity. The in-phase or the quadrature component of the signal is typically inverted to reconstruct either the electrical conductivity or the magnetic permeability of the soil [9,7,10].…”

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confidence: 99%

Inversion of Multiconfiguration Complex EMI Data with Minimum Gradient Support Regularization: A Case Study

et al. 2020

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Frequency-domain electromagnetic instruments allow the collection of data in different configurations, that is, varying the intercoil spacing, the frequency, and the height above the ground. Their handy size makes these tools very practical for near-surface characterization in many fields of applications, for example, precision agriculture, pollution assessments, and shallow geological investigations. To this end, the inversion of either the real (in-phase) or the imaginary (quadrature) component of the signal has already been studied. Furthermore, in many situations, a regularization scheme retrieving smooth solutions is blindly applied, without taking into account the prior available knowledge. The present work discusses an algorithm for the inversion of the complex signal in its entirety, as well as a regularization method that promotes the sparsity of the reconstructed electrical conductivity distribution. This regularization strategy incorporates a minimum gradient support stabilizer into a truncated generalized singular value decomposition scheme. The results of the implementation of this sparsity-enhancing regularization at each step of a damped Gauss-Newton inversion algorithm (based on a nonlinear forward model) are compared with the solutions obtained via a standard smooth stabilizer. An approach for estimating the depth of investigation, that is, the maximum depth that can be investigated by a chosen instrument configuration in a particular experimental setting, is also discussed. The effectiveness and limitations of the whole inversion algorithm are demonstrated on synthetic and real data sets.

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Regularized Inversion of Multi-Frequency EM Data in Geophysical Applications

Cited by 12 publications

References 7 publications

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Local- and Plot-Scale Measurements of Soil Moisture: Time and Spatially Resolved Field Techniques in Plain, Hill and Mountain Sites

Inversion of Multiconfiguration Complex EMI Data with Minimum Gradient Support Regularization: A Case Study

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