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

Research output: Contribution to conference › Conference abstract for conference › Research

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 conference › Conference abstract for conference › Research

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', GeoHydro 2011, Quebec, Canada, 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

Y2 - 28 August 2011

ER -

ID: 35449179