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Probabilistic 1-D inversion of frequency-domain electromagnetic data using a Kalman ensemble generator

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Abstract
Frequency-domain electromagnetic (FDEM) data of the subsurface are determined by electrical conductivity and magnetic susceptibility. We apply a Kalman ensemble generator (KEG) to 1-D probabilistic multilayer inversion of the FDEM data to simultaneously derive conductivity and susceptibility. The KEG provides an efficient alternative to an exhaustive Bayesian framework for FDEM inversion, including a measure for the uncertainty of the inversion result. In addition, the method provides a measure for the depth below which the measurement is insensitive to the parameters of the subsurface. This so-called depth of investigation is derived from ensemble covariances. Synthetic and field data examples reveal how the KEG approach can be applied to FDEM data and how FDEM calibration data and prior beliefs can be combined in the inversion procedure. For the field data set, many inversions for 1-D subsurface models are performed at neighboring measurement locations. Assuming identical prior models for these inversions, we save computational time by reusing the initial KEG ensemble across all measurement locations.
Keywords
Bayesian inversion, frequency-domain electromagnetics (FDEMs), Kalman ensemble generator (KEG), Monte Carlo, MAGNETIC-SUSCEPTIBILITY, EMI SURVEY, RESISTIVITY, DEPTH, CALIBRATION, PARAMETERS, DRIFT

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MLA
Bobe, Christin, et al. “Probabilistic 1-D Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 58, no. 5, 2020, pp. 3287–97, doi:10.1109/tgrs.2019.2953004.
APA
Bobe, C., Van De Vijver, E., Keller, J., Hanssens, D., Van Meirvenne, M., & De Smedt, P. (2020). Probabilistic 1-D inversion of frequency-domain electromagnetic data using a Kalman ensemble generator. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 58(5), 3287–3297. https://doi.org/10.1109/tgrs.2019.2953004
Chicago author-date
Bobe, Christin, Ellen Van De Vijver, Johannes Keller, Daan Hanssens, Marc Van Meirvenne, and Philippe De Smedt. 2020. “Probabilistic 1-D Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58 (5): 3287–97. https://doi.org/10.1109/tgrs.2019.2953004.
Chicago author-date (all authors)
Bobe, Christin, Ellen Van De Vijver, Johannes Keller, Daan Hanssens, Marc Van Meirvenne, and Philippe De Smedt. 2020. “Probabilistic 1-D Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58 (5): 3287–3297. doi:10.1109/tgrs.2019.2953004.
Vancouver
1.
Bobe C, Van De Vijver E, Keller J, Hanssens D, Van Meirvenne M, De Smedt P. Probabilistic 1-D inversion of frequency-domain electromagnetic data using a Kalman ensemble generator. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 2020;58(5):3287–97.
IEEE
[1]
C. Bobe, E. Van De Vijver, J. Keller, D. Hanssens, M. Van Meirvenne, and P. De Smedt, “Probabilistic 1-D inversion of frequency-domain electromagnetic data using a Kalman ensemble generator,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 58, no. 5, pp. 3287–3297, 2020.
@article{8640283,
  abstract     = {{Frequency-domain electromagnetic (FDEM) data of the subsurface are determined by electrical conductivity and magnetic susceptibility. We apply a Kalman ensemble generator (KEG) to 1-D probabilistic multilayer inversion of the FDEM data to simultaneously derive conductivity and susceptibility. The KEG provides an efficient alternative to an exhaustive Bayesian framework for FDEM inversion, including a measure for the uncertainty of the inversion result. In addition, the method provides a measure for the depth below which the measurement is insensitive to the parameters of the subsurface. This so-called depth of investigation is derived from ensemble covariances. Synthetic and field data examples reveal how the KEG approach can be applied to FDEM data and how FDEM calibration data and prior beliefs can be combined in the inversion procedure. For the field data set, many inversions for 1-D subsurface models are performed at neighboring measurement locations. Assuming identical prior models for these inversions, we save computational time by reusing the initial KEG ensemble across all measurement locations.}},
  author       = {{Bobe, Christin and Van De Vijver, Ellen and Keller, Johannes and Hanssens, Daan and Van Meirvenne, Marc and De Smedt, Philippe}},
  issn         = {{0196-2892}},
  journal      = {{IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}},
  keywords     = {{Bayesian inversion,frequency-domain electromagnetics (FDEMs),Kalman ensemble generator (KEG),Monte Carlo,MAGNETIC-SUSCEPTIBILITY,EMI SURVEY,RESISTIVITY,DEPTH,CALIBRATION,PARAMETERS,DRIFT}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{3287--3297}},
  title        = {{Probabilistic 1-D inversion of frequency-domain electromagnetic data using a Kalman ensemble generator}},
  url          = {{http://dx.doi.org/10.1109/tgrs.2019.2953004}},
  volume       = {{58}},
  year         = {{2020}},
}

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