Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation

Šmı́dl, Václav; Hofman, Radek

doi:10.1080/00401706.2013.860917

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…In reality, the measured value is a sum of activity from the release and a natural background radiation. We assume that the natural background radiation is μ bg = 10 −7 Sv/h (Šmídl and Hofman, ) with σ bg = 0.25 × 10 −7 . Moreover, the radiation dose sensors are corrupted by error of measurement which is typically proportional to the measured gamma dose by the factor in the range 7–20% (Thompson et al ).…”

Section: Twin Experiments Of Multi‐nuclide Releasementioning

confidence: 99%

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Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

Tichý

Šmı́dl

Hofman

et al. 2018

Quart J Royal Meteoro Soc

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Determination of a source term of an accidental release of radioactive material into the atmosphere is very important for evaluating emergency situations and their consequences. However, knowledge of the source term and its composition is typically vague and uncertain. One possible way to obtain the source term is inverse modeling in which an atmospheric transport model is combined with field measurements. The most accessible measurements are those from gamma dose rate (GDR) detectors. However, GDR measurements represent a sum of contribution from all nuclides from both plume and deposition which makes the problem particularly difficult. The same difficulty arises when the measurements can not distinguish contribution from another species in the release, such as nuclides attached to different particle sizes. We propose a Bayesian method for recovery of the source term from GDR measurements where a priori knowledge on ratios of different species is given in the form of bounds. This knowledge is incorporated into the model of covariance matrix of the source term. The Bayesian methodology allows to handle uncertain knowledge on the nuclide ratios as well as unknown temporal correlations of the source term. We evaluate and compare the proposed method with other state‐of‐the‐art methods on a twin experiment of a non‐stationary release of 16 nuclides from the Czech nuclear power plant Temelin being registered by the Austrian GDR monitoring network. Real‐world validation of the approach is performed on the latest measurements of concentration and deposition of caesium‐137 from the Chernobyl accident, where we estimate composition of the source term from different particle sizes (species). The estimated source term is in very good agreement with previously reported results and the calculated species ratios are supported by the available observations.

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Section: Twin Experiments Of Multi‐nuclide Releasementioning

confidence: 99%

“…The Monte Carlo approach to estimation of the source term from GDR has been presented e.g. by Šmídl and Hofman (2014) but only for a single nuclide scenario.…”

Section: Introductionmentioning

confidence: 99%

Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

Tichý

Šmı́dl

Hofman

et al. 2018

Quart J Royal Meteoro Soc

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“…Another attempt that we made in this paper to improve convergence speed of the adaptive importance sampling schemes is to regularize the estimation of the parameters of the importance distribution as illustrated in Šmídl and Hofman (2014). The regularization stems from the use of an informative prior on γ of the importance distribution q t (γ) of MAMIS and treat the update of these parameters in a Bayesian fashion (Kulhavý 1996).…”

Section: Convergence Of Samplers For Gp Regressionmentioning

confidence: 99%

Adaptive multiple importance sampling for Gaussian processes

Xiong

Šmı́dl

Filippone

2017

Journal of Statistical Computation and Simulation

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In applications of Gaussian processes where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. Normally, this is done by means of standard Markov chain Monte Carlo (MCMC) algorithms. Motivated by the issues related to the complexity of calculating the marginal likelihood that can make MCMC algorithms inefficient, this paper develops an alternative inference framework based on Adaptive Multiple Importance Sampling (AMIS). This paper studies the application of AMIS in the case of a Gaussian likelihood, and proposes the Pseudo-Marginal AMIS for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios and remains competitive for moderately large dimensional parameter spaces.

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Sparse optimization for inverse problems in atmospheric modelling

Adam

Branda

2016

Environmental Modelling & Software

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Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation

Cited by 9 publications

References 30 publications

Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

Adaptive multiple importance sampling for Gaussian processes

Sparse optimization for inverse problems in atmospheric modelling

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