Stochastic inversion of vadose zone properties: Impact of parameter correlation on uncertainty estimates
Détails
ID Serval
serval:BIB_2CA746BE4A29
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Stochastic inversion of vadose zone properties: Impact of parameter correlation on uncertainty estimates
Editeur
EAGE
Organisation
16th European Meeting of Environmental and Engineering Geophysics, Zurich, Switzerland
Statut éditorial
Publié
Date de publication
2010
Langue
anglais
Notes
Scholer2010
Résumé
A number of studies have shown that time-lapse crosshole geophysical
data can provide valuable information regarding the hydraulic properties
of the unsaturated zone. The stochastic inversion of such data can
yield estimates of uncertainties in such properties, which are valuable
for hydrological characterization. Here, we investigate the effect
on output parameter uncertainties of accounting for realistic correlation
between the hydraulic model parameters in the inversion procedure.
We do this within a Bayesian framework using a Markov-chain-Monte-Carlo
(McMC) strategy, and we investigate the particular problem of estimating
vadose zone hydraulic properties from ground-penetrating radar (GPR)
data collected during a 1-D infiltration experiment. Our results
clearly indicate that prior information on the correlation between
model parameters has the effect of noticeably reducing posterior
parameter uncertainties and hence, if available, should be included
in such inversions.
data can provide valuable information regarding the hydraulic properties
of the unsaturated zone. The stochastic inversion of such data can
yield estimates of uncertainties in such properties, which are valuable
for hydrological characterization. Here, we investigate the effect
on output parameter uncertainties of accounting for realistic correlation
between the hydraulic model parameters in the inversion procedure.
We do this within a Bayesian framework using a Markov-chain-Monte-Carlo
(McMC) strategy, and we investigate the particular problem of estimating
vadose zone hydraulic properties from ground-penetrating radar (GPR)
data collected during a 1-D infiltration experiment. Our results
clearly indicate that prior information on the correlation between
model parameters has the effect of noticeably reducing posterior
parameter uncertainties and hence, if available, should be included
in such inversions.
Création de la notice
25/11/2013 18:31
Dernière modification de la notice
21/08/2019 6:17
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