The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling

Caers, Jef; Hoffman, Todd

doi:10.1007/s11004-005-9005-9

Cited by 155 publications

(74 citation statements)

References 9 publications

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“…Note that the same conclusions can be made using optimization based on the gradual deformation method [29] and the probability perturbation method [6], which are two methods that can be used to gradually change a realization of a random function based on 2-point and multiple-point geostatistics respectively. Likewise the optimization method proposed by Mariethoz et al [30], based on a prior sampler resembling the sequential Gibbs sampler, will not locate the model with maximum posterior probability.…”

Section: Cross Borehole Tomographymentioning

confidence: 99%

Inverse problems with non-trivial priors: efficient solution through sequential Gibbs sampling

2012

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Section: Cross Borehole Tomographymentioning

confidence: 99%

Inverse problems with non-trivial priors: efficient solution through sequential Gibbs sampling

2012

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“…The sensitivity analysis for the Aguamilpa water quality model was conducted using the traditional grid perturbation method (Caers and Hoffman, 2006), which consisted of fluctuating each of the defined parameters in Tab. 2.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Sensitivity and uncertainty analysis on water quality modelling of Aguamilpa reservoir

Rangel‐Peraza

Anda²,

González‐Farías

et al. 2016

J Limnol

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“…Several studies have used it : Hu et al [2001], Caers [2003], and Caers and Hoffman [2006] (probability perturbation method is used in conjunction with the gradual deformation method to generate realizations that improve data match).…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of plume distribution for carbon sequestration using parameter estimation with limited monitoring data

Espinet

Shoemaker

Doughty

2013

Water Resources Research

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[1] This study develops and evaluates an integrated methodology including parameter zonation, efficient global optimization, and multiple aquifer realizations that gives a useful forecast of the pressure distribution and the CO 2 plume migration through a heterogeneous storage formation, over time, using only a few monitoring locations in a feasibly short amount of computing time, while dealing with data error. Geological characteristics of the example application are similar to a CO 2 sequestration pilot test conducted in a fluvialdeltaic geological setting. In the example, the CO 2 injection problem is simulated with the TOUGH2 code, a numerical simulator for multiphase (gas and aqueous), multicomponent flow and transport. The inverse problem is difficult because the GCS optimization estimation problem has multiple local minima and the simulation is computationally expensive. The efficient surrogate response surface global optimization algorithm ''Stochastic RBF'' (stochastic radial basis function) is used to calibrate the model parameters. Results are averaged over three saline aquifer realizations. In the third numerical experiment using only pressure data, the CO 2 plume could be determined with an average correlation coefficient compared to the actual plume of up to R 2 ¼ 0.916 (for current plume at t ¼ 1.5 years) and average R 2 ¼ 0.80 (for forecasted plume estimated at t ¼ 7.5 years years, using only the first 1.5 years of monitoring data). Adding gas saturation data improved the 6 year forecast somewhat (increasing average R 2 ¼ 0.85) but it was not significantly helpful in estimating the current plume. Both our inverse methodology and findings can be broadly applicable to GCS in heterogeneous sedimentary formations.Citation: Espinet, A., C. Shoemaker, and C. Doughty (2013), Estimation of Plume distribution for carbon sequestration using parameter estimation with limited monitoring data, Water Resour. Res., 49,

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The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling

Cited by 155 publications

References 9 publications

Inverse problems with non-trivial priors: efficient solution through sequential Gibbs sampling

Inverse problems with non-trivial priors: efficient solution through sequential Gibbs sampling

Sensitivity and uncertainty analysis on water quality modelling of Aguamilpa reservoir

Estimation of plume distribution for carbon sequestration using parameter estimation with limited monitoring data

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