State and Parameter Estimation with an SIR Particle Filter in a Three-Dimensional Groundwater Pollutant Transport Model

Chang, Shoou‐Yuh; Chowhan, Tushar; Latif, Sikdar

doi:10.1061/(asce)ee.1943-7870.0000584

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…These schemes vary in structure and complexity depending on the nonlinearity, complexity, and dimension of the system. An assimilation scheme that recently drew attention is the particle filter (PF), which is capable of handling any type of statistical distribution, Gaussian or not, making it well suited for strongly nonlinear systems [ Montzka et al ., ; Chang et al ., ; Moradkhani et al ., ]. The PF requires, however, a large set of particles to accurately sample the distribution of the state and parameters, making this scheme computationally intensive for large‐scale hydrological applications.…”

Section: Introductionmentioning

confidence: 99%

Constraining a compositional flow model with flow‐chemical data using an ensemble‐based Kalman filter

Gharamti

Kadoura

Valstar³

et al. 2014

Water Resources Research

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Isothermal compositional flow models require coupling transient compressible flows and advective transport systems of various chemical species in subsurface porous media. Building such numerical models is quite challenging and may be subject to many sources of uncertainties because of possible incomplete representation of some geological parameters that characterize the system's processes. Advanced data assimilation methods, such as the ensemble Kalman filter (EnKF), can be used to calibrate these models by incorporating available data. In this work, we consider the problem of estimating reservoir permeability using information about phase pressure as well as the chemical properties of fluid components. We carry out state-parameter estimation experiments using joint and dual updating schemes in the context of the EnKF with a two-dimensional single-phase compositional flow model (CFM). Quantitative and statistical analyses are performed to evaluate and compare the performance of the assimilation schemes.Our results indicate that including chemical composition data significantly enhances the accuracy of the permeability estimates. In addition, composition data provide more information to estimate system states and parameters than do standard pressure data. The dual state-parameter estimation scheme provides about 10% more accurate permeability estimates on average than the joint scheme when implemented with the same ensemble members, at the cost of twice more forward model integrations. At similar computational cost, the dual approach becomes only beneficial after using large enough ensembles.

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Section: Introductionmentioning

confidence: 99%

Constraining a compositional flow model with flow‐chemical data using an ensemble‐based Kalman filter

Gharamti

Kadoura

Valstar³

et al. 2014

Water Resources Research

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“…The matched pattern is directly sampled from the ensemble of training images, and thus the curvilinear structures could be preserved through the process of data conditioning. This distinguishes the method from non-Gaussian particle filter methodology (Moradkhani et al, 2012;Chang et al, 2012) where each particle (i.e., the realization) is sampled from the posterior distribution by evaluating the likelihood. As we know, particle filtering can only be applied to low-dimensional cases because of the problem of filter divergence.…”

Section: Discussionmentioning

confidence: 99%

Two-point or multiple-point statistics? A comparison between the ensemble Kalman filtering and the ensemble pattern matching inverse methods

Srinivasan

Zhou

et al. 2015

Advances in Water Resources

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ElsevierLi ., L.; Zhou ., H.; Gómez-Hernández, JJ. (2015). Two-point AbstractThe Ensemble Kalman Filter (EnKF) has been commonly used to assimilate real time dynamic data into geologic models over the past decade. Despite its various advantages such as computational efficiency and its capability to handle multiple sources of uncertainty, the EnKF may not be used to reliably update models that are characterized by curvilinear geometries such as fluvial deposits where the permeable channels play a crucial role in the prediction of solute transport. It is well-known that the EnKF performs optimally for updating multi-Gaussian distributed fields, basically because it uses two-point statistics (i.e., covariances) to represent the relationship between the model parameters and between the model parameters and the observed response, and this is the only statistic necessary to fully characterize a multiGaussian distribution.The Ensemble PATtern matching (EnPAT) is an alternative ensemble based method that shows significant potential to condition complex geology such as channelized aquifers to dynamic data. The EnPAT is an evolution of the EnKF, replacing, in the analysis step, two-point statistics with multiple-point statistics.The advantages of EnPAT reside in its capability to honor the complex spatial connectivity of geologic structures as well as the measured static and dynamic data. In this work, the performance of the classical EnKF and the EnPAT are compared for modeling a synthetic channelized aquifer. The results reveal that the EnPAT yields better prediction of transport characteristics than the EnKF because it characterizes the conductivity heterogeneity better. Issues such as uncertainty of multiple variables and the effect of measurement errors on EnPAT results will be discussed.

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“…But the often larger number of > 100 simulations required by SCM compared to EnKF (Liu et al, 2012) has limited its application to CPU-intensive real-world groundwater models. Example of SMC application in groundwater MDI problems includes Chang et al (2012) and Abbaszadeh et al (2018). SMC is popular in some engineering fields such as tracking and signal processing (Djuric et al, 2003), and it has also been applied to many hydrologic problems (Zhou et al, 2006;Smith et al, 2008).…”

Section: Numerical Approximations For the Bayesian Filtermentioning

confidence: 99%

Model-data interaction in groundwater studies: Review of methods, applications and future directions

Rajabi

Ataie-Ashtiani

Simmons

2018

Journal of Hydrology

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We define model-data interaction (MDI) as a two way process between models and data, in which on one hand data can serve the modeling purpose by supporting model discrimination, parameter refinement, uncertainty analysis, etc., and on the other hand models provide a tool for data fusion, interpretation, interpolation, etc. MDI has many applications in the realm of groundwater and has been the topic of extensive research in the groundwater community for the past several decades. This has led to the development of a multitude of increasingly sophisticated methods. The progress of data acquisition technologies and the evolution of models are continuously changing the landscape of groundwater MDI, creating new challenges and opportunities that must be properly understood and addressed. This paper aims to review, analyze and classify research on MDI in groundwater applications, and discusses several related aspects including: (1) basic theoretical concepts and classification of methods, (2) sources of uncertainty and how they are commonly addressed, (3) specific characteristics of groundwater models and data that affect the choice of methods, (4) how models and data can interact to provide added value in groundwater applications, (5) software and codes for MDI, and (6) key issues that will likely form future research directions. The review shows that there are many tools and techniques for groundwater MDI, and this diversity is needed to support different MDI objectives, assumptions, model and data types and computational constraints. The study identifies eight categories of applications for MDI in the groundwater literature, and highlights the growing gap between MDI practices in the research community and those in consulting, industry and government.

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State and Parameter Estimation with an SIR Particle Filter in a Three-Dimensional Groundwater Pollutant Transport Model

Cited by 16 publications

References 27 publications

Constraining a compositional flow model with flow‐chemical data using an ensemble‐based Kalman filter

Constraining a compositional flow model with flow‐chemical data using an ensemble‐based Kalman filter

Two-point or multiple-point statistics? A comparison between the ensemble Kalman filtering and the ensemble pattern matching inverse methods

Model-data interaction in groundwater studies: Review of methods, applications and future directions

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