Calibration and filtering strategies for frequency domain electromagnetic data

Minsley, Burke J.; Smith, Bruce D.; Hammack, Richard; Sams, James I.; Veloski, Garret

doi:10.1016/j.jappgeo.2012.01.008

Cited by 72 publications

(33 citation statements)

References 29 publications

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“…We stress that these findings are specific to the assumptions underlying our synthetic example, e.g., the electrical conductivities, measurement error, and measurement frequencies. We expect DOI could be improved by consideration of lower frequencies, larger coil spacings, or higherpower instruments which might improve signal-to-noise; furthermore, additional calibration (Minsley et al, 2010) might reduce measurement error.…”

Section: Doi Analysismentioning

confidence: 99%

“…McNeill (1996) maintains that data from multiple frequencies, at fixed separation, contain mostly redundant information because depth sensitivity is controlled strongly by coil spacing and that megahertz-range data would be required for meaningful depth-dependent information. Nonetheless, a number of practitioners have extracted layer information from multi-frequency EM data (Huang, 2005;Huang and Won, 2003;Martinelli and Duplaa, 2008;Minsley et al, 2010). Abraham et al (2006) and Minsley et al (2010) produced images of resistivity versus depth from multi-frequency EM data by calibrating the EM data based on the theoretical response estimated from a direct current (DC) resistivity sounding.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inversion of multi-frequency electromagnetic induction data for 3D characterization of hydraulic conductivity

Brosten

Day‐Lewis

Schultz

et al. 2011

Journal of Applied Geophysics

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Section: Doi Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inversion of multi-frequency electromagnetic induction data for 3D characterization of hydraulic conductivity

Brosten

Day‐Lewis

Schultz

et al. 2011

Journal of Applied Geophysics

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“…Many previous land EM applications interpreted the data in the time domain (e.g., Keller et al, 1984;Hördt et al, 2000; and used magnetic dipole sources (Kurtz et al, 1989;Mitsuhata et al, 2006;Minsley et al, 2012;Perez-Flores et al, 2012). Unfortunately, standard transient EM (TEM) configurations with magnetic dipole sources have limited capability of resolving resistors (Chave, 2009).…”

Section: Introductionmentioning

confidence: 99%

3D inversion and resolution analysis of land-based CSEM data from the Ketzin CO2 storage formation

2014

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We evaluated 3D inversion of land controlled-source electromagnetic (CSEM) data collected across the Ketzin CO 2 storage formation. A newly developed, parallel and distributed 3D inversion code, which is based on a direct forward solver, has been used. This inversion scheme allowed us to calculate the Jacobian matrix explicitly within a reasonable time and use it to calculate regularization parameters, inspect survey coverage, and carry out resolution analysis. After demonstrating that the magnetic field components are sensitive to conductors only, whereas the electric field components are sensitive to all features of interest, we continued to work with electric field data only. Estimates of data uncertainty obtained from robust processing were used for automated data preselection and weighting during inversion. We tested different regularization techniques and a range of starting models to explore the model space and assess the influence of regularization on the inversion images. We further demonstrated an approach for handling numerical singularities due to sources located inside the inversion domain. We estimated survey coverage, horizontal and vertical resolution, and depth penetration using cumulative sensitivity, point spread functions, and depth-resolution plots. Based on data fit analysis, we determined a preferred subsurface conductivity model, which we compared to an independent regional structural geologic model, and we provided an interpretation for the structures resolved. The inversion approach we used provides robust results in good agreement with known geology, offers new possibilities for model assessment, and should be transferable to other CSEM data sets.

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“…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).…”

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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Calibration and filtering strategies for frequency domain electromagnetic data

Cited by 72 publications

References 29 publications

Inversion of multi-frequency electromagnetic induction data for 3D characterization of hydraulic conductivity

Inversion of multi-frequency electromagnetic induction data for 3D characterization of hydraulic conductivity

3D inversion and resolution analysis of land-based CSEM data from the Ketzin CO2 storage formation

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

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