Rights statement: Copyright 2011 by the American Geophysical Union.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Estimating vadose zone hydraulic properties using ground penetrating radar
T2 - the impact of prior information
AU - Scholer, Marie
AU - Irving, James
AU - Binley, Andrew
AU - Holliger, Klaus
N1 - Copyright 2011 by the American Geophysical Union.
PY - 2011/10
Y1 - 2011/10
N2 - A number of geophysical methods, such as ground-penetrating radar (GPR), have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, the stochastic inversion of such data within a coupled geophysical-hydrological framework may allow for the effective estimation of vadose zone hydraulic parameters and their corresponding uncertainties. A critical issue in stochastic inversion is choosing prior parameter probability distributions from which potential model configurations are drawn and tested against observed data. A well chosen prior should reflect as honestly as possible the initial state of knowledge regarding the parameters and be neither overly specific nor too conservative. In a Bayesian context, combining the prior with available data yields a posterior state of knowledge about the parameters, which can then be used statistically for predictions and risk assessment. Here we investigate the influence of prior information regarding the van Genuchten-Mualem (VGM) parameters, which describe vadose zone hydraulic properties, on the stochastic inversion of crosshole GPR data collected under steady state, natural-loading conditions. We do this using a Bayesian Markov chain Monte Carlo (MCMC) inversion approach, considering first noninformative uniform prior distributions and then more informative priors derived from soil property databases. For the informative priors, we further explore the effect of including information regarding parameter correlation. Analysis of both synthetic and field data indicates that the geophysical data alone contain valuable information regarding the VGM parameters. However, significantly better results are obtained when we combine these data with a realistic, informative prior.
AB - A number of geophysical methods, such as ground-penetrating radar (GPR), have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, the stochastic inversion of such data within a coupled geophysical-hydrological framework may allow for the effective estimation of vadose zone hydraulic parameters and their corresponding uncertainties. A critical issue in stochastic inversion is choosing prior parameter probability distributions from which potential model configurations are drawn and tested against observed data. A well chosen prior should reflect as honestly as possible the initial state of knowledge regarding the parameters and be neither overly specific nor too conservative. In a Bayesian context, combining the prior with available data yields a posterior state of knowledge about the parameters, which can then be used statistically for predictions and risk assessment. Here we investigate the influence of prior information regarding the van Genuchten-Mualem (VGM) parameters, which describe vadose zone hydraulic properties, on the stochastic inversion of crosshole GPR data collected under steady state, natural-loading conditions. We do this using a Bayesian Markov chain Monte Carlo (MCMC) inversion approach, considering first noninformative uniform prior distributions and then more informative priors derived from soil property databases. For the informative priors, we further explore the effect of including information regarding parameter correlation. Analysis of both synthetic and field data indicates that the geophysical data alone contain valuable information regarding the VGM parameters. However, significantly better results are obtained when we combine these data with a realistic, informative prior.
KW - TIME-DOMAIN REFLECTOMETRY
KW - CONDUCTIVITY
KW - BOREHOLE GEOPHYSICAL METHODS
KW - DISTRIBUTIONS
KW - MODEL
KW - SOIL-WATER CONTENT
KW - PARAMETERS
KW - FLOW
KW - UNSATURATED SANDSTONE
KW - UNCERTAINTY
KW - GPR
KW - MCMC
KW - prior information
KW - stochastic inversion
KW - vadose zone
U2 - 10.1029/2011WR010409
DO - 10.1029/2011WR010409
M3 - Journal article
VL - 47
JO - Water Resources Research
JF - Water Resources Research
SN - 0043-1397
IS - 10
M1 - W10512
ER -