High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals

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High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals. / Laloy, Eric; Huisman, Johan Alexander; Jacques, Diederik.

In: Journal of Hydrology, Vol. 519, No. Part B, 11.2014, p. 2121-2135.

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Laloy, Eric ; Huisman, Johan Alexander ; Jacques, Diederik. / High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals. In: Journal of Hydrology. 2014 ; Vol. 519, No. Part B. pp. 2121-2135.

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@article{9b85c7a7229a43dabf8575502d565844,
title = "High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals",
abstract = "This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04cm³cm-3. This RMSE value reduces to less than 0.02cm³cm-3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors.",
keywords = "Spatial TDR, full waveform inversion, MCMC, soil moisture profile",
author = "Eric Laloy and Huisman, {Johan Alexander} and Diederik Jacques",
note = "Score = 10",
year = "2014",
month = nov,
doi = "10.1016/j.jhydrol.2014.10.005",
language = "English",
volume = "519",
pages = "2121--2135",
journal = "Journal of Hydrology",
issn = "0022-1694",
publisher = "Elsevier",
number = "Part B",

}

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TY - JOUR

T1 - High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals

AU - Laloy, Eric

AU - Huisman, Johan Alexander

AU - Jacques, Diederik

N1 - Score = 10

PY - 2014/11

Y1 - 2014/11

N2 - This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04cm³cm-3. This RMSE value reduces to less than 0.02cm³cm-3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors.

AB - This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04cm³cm-3. This RMSE value reduces to less than 0.02cm³cm-3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors.

KW - Spatial TDR

KW - full waveform inversion

KW - MCMC

KW - soil moisture profile

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_137266

UR - http://knowledgecentre.sckcen.be/so2/bibref/11922

U2 - 10.1016/j.jhydrol.2014.10.005

DO - 10.1016/j.jhydrol.2014.10.005

M3 - Article

VL - 519

SP - 2121

EP - 2135

JO - Journal of Hydrology

JF - Journal of Hydrology

SN - 0022-1694

IS - Part B

ER -

ID: 329168