Data-worth analysis through probabilistic collocation-based Ensemble Kalman Filter
暂无分享,去创建一个
Alberto Guadagnini | Dongxiao Zhang | Cheng Dai | Liang Xue | Dongxiao Zhang | A. Guadagnini | C. Dai | L. Xue
[1] R. Ghanem,et al. Stochastic Finite Elements: A Spectral Approach , 1990 .
[2] Y. Rubin,et al. A hypothesis‐driven approach to optimize field campaigns , 2012 .
[3] Bruno Sudret,et al. Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..
[4] Alberto Guadagnini,et al. Global sensitivity analysis through polynomial chaos expansion of a basin-scale geochemical compaction model , 2013, Computational Geosciences.
[5] You‐Kuan Zhang. Stochastic Methods for Flow in Porous Media: Coping with Uncertainties , 2001 .
[6] Adrian Sandu,et al. Parametric Design Optimization of Uncertain Ordinary Differential Equation Systems , 2012 .
[7] Dongxiao Zhang,et al. Efficient and Accurate Global Sensitivity Analysis for Reservoir Simulations By Use of Probabilistic Collocation Method , 2014 .
[8] Seong Jin Noh,et al. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .
[9] Alberto Guadagnini,et al. Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation , 2014 .
[10] Louis J. Durlofsky,et al. Use of Reduced-order Models for Improved Data Assimilation within an EnKF Context , 2011, ANSS 2011.
[11] Dongbin Xiu,et al. A generalized polynomial chaos based ensemble Kalman filter with high accuracy , 2009, J. Comput. Phys..
[12] Alberto Guadagnini,et al. Conditioning mean steady state flow on hydraulic head and conductivity through geostatistical inversion , 2003 .
[13] Karim C. Abbaspour,et al. A Bayesian approach for incorporating uncertainty and data worth in environmental projects , 1996 .
[14] Alberto Guadagnini,et al. Comparative analysis of formulations for conservative transport in porous media through sensitivity‐based parameter calibration , 2013 .
[15] Alberto Guadagnini,et al. Inverse Analysis of Stochastic Moment Equations for Transient Flow in Randomly Heterogeneous Media , 2009 .
[16] Alberto Guadagnini,et al. Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow , 2013 .
[17] Dean S. Oliver,et al. THE ENSEMBLE KALMAN FILTER IN RESERVOIR ENGINEERING-A REVIEW , 2009 .
[18] Lingzao Zeng,et al. A stochastic collocation based Kalman filter for data assimilation , 2010 .
[19] Thomas Schaaf,et al. Using Experimental Designs, Assisted History Matching Tools and Bayesian Framework to get Probabilistic Production Forecasts , 2008 .
[20] Patrick M. Reed,et al. Many‐objective groundwater monitoring network design using bias‐aware ensemble Kalman filtering, evolutionary optimization, and visual analytics , 2011 .
[21] Dongxiao Zhang,et al. A sparse grid based Bayesian method for contaminant source identification , 2012 .
[22] Harrie-Jan Hendricks Franssen,et al. Jointly Mapping Hydraulic Conductivity and Porosity by Assimilating Concentration Data via Ensemble Kalman Filter , 2012 .
[23] R. Freeze,et al. The worth of data in predicting aquitard continuity in hydrogeological design , 1993 .
[24] Enrico Zio,et al. Polynomial chaos expansion for global sensitivity analysis applied to a model of radionuclide migration in a randomly heterogeneous aquifer , 2013, Stochastic Environmental Research and Risk Assessment.
[25] Wolfgang Nowak,et al. Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design , 2012 .
[26] Roger Ghanem,et al. Characterization of reservoir simulation models using a polynomial chaos‐based ensemble Kalman filter , 2009 .
[27] Ning Liu,et al. Inverse Theory for Petroleum Reservoir Characterization and History Matching , 2008 .
[28] Dongxiao Zhang,et al. Probabilistic collocation method for flow in porous media: Comparisons with other stochastic methods , 2007 .
[29] Bangti Jin,et al. Fast Bayesian approach for parameter estimation , 2008 .
[30] R. Ghanem,et al. Quantifying uncertainty in chemical systems modeling , 2004 .
[31] Haibin Chang,et al. A Probabilistic Collocation-Based Kalman Filter for History Matching , 2011 .
[32] Thomas A. Zang,et al. Stochastic approaches to uncertainty quantification in CFD simulations , 2005, Numerical Algorithms.
[33] Liangping Li,et al. Inverse methods in hydrogeology: Evolution and recent trends , 2014 .
[34] Pär-Erik Back,et al. A model for estimating the value of sampling programs and the optimal number of samples for contaminated soil , 2007 .
[35] Y. Rubin,et al. A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields , 2010 .
[36] Heng Li,et al. Efficient and Accurate Quantification of Uncertainty for Multiphase Flow With the Probabilistic Collocation Method , 2009 .
[37] Sergey Oladyshkin,et al. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion , 2012, Reliab. Eng. Syst. Saf..
[38] Habib N. Najm,et al. Stochastic spectral methods for efficient Bayesian solution of inverse problems , 2005, J. Comput. Phys..
[39] Y. Rubin. Transport in heterogeneous porous media: Prediction and uncertainty , 1991 .
[40] Menner A. Tatang,et al. An efficient method for parametric uncertainty analysis of numerical geophysical models , 1997 .
[41] D. Xiu,et al. Modeling Uncertainty in Steady State Diffusion Problems via Generalized Polynomial Chaos , 2002 .
[42] Ming Ye,et al. An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling , 2013 .
[43] Alberto Guadagnini,et al. Multimodel Bayesian analysis of groundwater data worth , 2014 .
[44] Dongxiao Zhang,et al. Stochastic analysis of unsaturated flow with probabilistic collocation method , 2009 .
[45] S. P. Neuman,et al. estimation of spatial covariance structures by adjoint state maximum likelihood cross validation: 1. Theory , 1989 .
[46] Alberto Guadagnini,et al. Use of global sensitivity analysis and polynomial chaos expansion for interpretation of nonreactive transport experiments in laboratory‐scale porous media , 2011 .
[47] Christian D Langevin,et al. Quantifying Data Worth Toward Reducing Predictive Uncertainty , 2010, Ground water.
[48] Alan J. Rabideau,et al. Decision Analysis for Pump‐and‐Treat Design , 2000 .
[49] D. A. Zimmerman,et al. A comparison of seven geostatistically based inverse approaches to estimate transmissivities for modeling advective transport by groundwater flow , 1998 .
[50] Haibin Chang,et al. History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique , 2014, Computational Geosciences.
[51] Sergey Oladyshkin,et al. Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology , 2012 .
[52] Holger Class,et al. Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations , 2013, Computational Geosciences.
[53] Y. Marzouk,et al. A stochastic collocation approach to Bayesian inference in inverse problems , 2009 .
[54] S. P. Neuman,et al. Role of model selection criteria in geostatistical inverse estimation of statistical data‐ and model‐parameters , 2011 .
[55] Dongxiao Zhang,et al. Investigation of flow and transport processes at the MADE site using ensemble Kalman filter , 2008 .
[56] Roger Ghanem,et al. Scales of fluctuation and the propagation of uncertainty in random porous media , 1998 .
[57] Cheng Chen,et al. Efficient Bayesian experimental design for contaminant source identification , 2015, Water Resources Research.
[58] S. P. Neuman,et al. Bayesian analysis of data-worth considering model and parameter uncertainties , 2012 .
[59] Andres Alcolea,et al. Pilot points method incorporating prior information for solving the groundwater flow inverse problem , 2006 .
[60] Heng Li,et al. A Comparative Study of the Probabilistic-Collocation and Experimental-Design Methods for Petroleum-Reservoir Uncertainty Quantification , 2011 .
[61] Xin Wu,et al. Systematic assessment of the uncertainty in integrated surface water‐groundwater modeling based on the probabilistic collocation method , 2014 .
[62] H. H. Franssen,et al. A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments , 2009 .
[63] Guang Lin,et al. An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling , 2014, J. Comput. Phys..
[64] Xianhong Xie,et al. Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter , 2010 .
[65] P. J. Chilton,et al. Groundwater: the processes and global significance of aquifer degradation. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[66] Liang Xue,et al. A multimodel data assimilation framework via the ensemble Kalman filter , 2014 .
[67] Clayton V. Deutsch,et al. GSLIB: Geostatistical Software Library and User's Guide , 1993 .
[68] Yan Chen,et al. Data assimilation for transient flow in geologic formations via ensemble Kalman filter , 2006 .
[69] S. P. Neuman,et al. Inverse stochastic moment analysis of steady state flow in randomly heterogeneous media , 2006 .
[70] Y. Rubin,et al. Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain , 2010 .
[71] A Blocking Markov Chain Monte Carlo Method for Inverse Stochastic Hydrogeological Modeling , 2009 .
[72] N. Wiener. The Homogeneous Chaos , 1938 .
[73] Salvador Pintos,et al. Global sensitivity analysis of Alkali–Surfactant–Polymer enhanced oil recovery processes , 2007 .