Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone

Research output: Contribution to conferenceConference abstract for conferenceResearch

Standard

Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone. / Scholer, Marie; Irving, James; Zibar, Majken Caroline Looms; Nielsen, Lars; Holliger, Klaus.

2011. Abstract from GeoHydro 2011, Quebec, Canada, .

Research output: Contribution to conferenceConference abstract for conferenceResearch

Harvard

Scholer, M, Irving, J, Zibar, MCL, Nielsen, L & Holliger, K 2011, 'Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone', 28/08/2011, .

APA

Scholer, M., Irving, J., Zibar, M. C. L., Nielsen, L., & Holliger, K. (2011). Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone. Abstract from GeoHydro 2011, Quebec, Canada, .

Vancouver

Scholer M, Irving J, Zibar MCL, Nielsen L, Holliger K. Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone. 2011. Abstract from GeoHydro 2011, Quebec, Canada, .

Author

Scholer, Marie ; Irving, James ; Zibar, Majken Caroline Looms ; Nielsen, Lars ; Holliger, Klaus. / Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone. Abstract from GeoHydro 2011, Quebec, Canada, .7 p.

Bibtex

@conference{6e5950aef627422f8c97a5d782a36094,
title = "Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone",
abstract = "Geophysical methods have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, time-lapse geophysical data, when coupled with a hydrological model and inverted stochastically, may allow for the effective estimation of subsurface hydraulic parameters and their corresponding uncertainties. In this study, we use a Bayesian Markov-chain-Monte-Carlo (MCMC) inversion approach to investigate how much information regarding vadose zone hydraulic properties can be retrieved from time-lapse crosshole GPR data collected at the Arrenaes field site in Denmark during a forced infiltration experiment.",
author = "Marie Scholer and James Irving and Zibar, {Majken Caroline Looms} and Lars Nielsen and Klaus Holliger",
year = "2011",
language = "English",
note = "null ; Conference date: 28-08-2011",

}

RIS

TY - ABST

T1 - Bayesian Markov chain Monte Carlo Inversion of Time-Lapse Geophysical Data To Characterize the Vadose Zone

AU - Scholer, Marie

AU - Irving, James

AU - Zibar, Majken Caroline Looms

AU - Nielsen, Lars

AU - Holliger, Klaus

PY - 2011

Y1 - 2011

N2 - Geophysical methods have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, time-lapse geophysical data, when coupled with a hydrological model and inverted stochastically, may allow for the effective estimation of subsurface hydraulic parameters and their corresponding uncertainties. In this study, we use a Bayesian Markov-chain-Monte-Carlo (MCMC) inversion approach to investigate how much information regarding vadose zone hydraulic properties can be retrieved from time-lapse crosshole GPR data collected at the Arrenaes field site in Denmark during a forced infiltration experiment.

AB - Geophysical methods have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, time-lapse geophysical data, when coupled with a hydrological model and inverted stochastically, may allow for the effective estimation of subsurface hydraulic parameters and their corresponding uncertainties. In this study, we use a Bayesian Markov-chain-Monte-Carlo (MCMC) inversion approach to investigate how much information regarding vadose zone hydraulic properties can be retrieved from time-lapse crosshole GPR data collected at the Arrenaes field site in Denmark during a forced infiltration experiment.

M3 - Conference abstract for conference

ER -

ID: 35449179