46 citations as recorded by crossref.

1. Inverse sequential simulation: Performance and implementation details T. Xu & J. Gómez-Hernández 10.1016/j.advwatres.2015.04.015
2. The power of transient piezometric head data in inverse modeling: An application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field T. Xu et al. 10.1016/j.advwatres.2013.01.006
3. Bayesian Inversion of Multi‐Gaussian Log‐Conductivity Fields With Uncertain Hyperparameters: An Extension of Preconditioned Crank‐Nicolson Markov Chain Monte Carlo With Parallel Tempering S. Xiao et al. 10.1029/2021WR030313
4. Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations T. Nguyen et al. 10.1109/TGRS.2023.3338296
5. Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter Z. Chen et al. 10.1007/s11004-021-09928-y
6. Indicator geostatistics for reconstructing Baton Rouge aquifer-fault hydrostratigraphy, Louisiana, USA A. Elshall et al. 10.1007/s10040-013-1037-5
7. Constructive epistemic modeling of groundwater flow with geological structure and boundary condition uncertainty under the Bayesian paradigm A. Elshall & F. Tsai 10.1016/j.jhydrol.2014.05.027
8. Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter D. Erdal & O. Cirpka 10.5194/hess-20-555-2016
9. On the role of patterns in understanding the functioning of soil-vegetation-atmosphere systems H. Vereecken et al. 10.1016/j.jhydrol.2016.08.053
10. Characterisation of river–aquifer exchange fluxes: The role of spatial patterns of riverbed hydraulic conductivities Q. Tang et al. 10.1016/j.jhydrol.2015.08.019
11. Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter F. Zovi et al. 10.1016/j.jhydrol.2017.02.056
12. Two-point or multiple-point statistics? A comparison between the ensemble Kalman filtering and the ensemble pattern matching inverse methods L. Li et al. 10.1016/j.advwatres.2015.05.014
13. Contaminant source and aquifer characterization: An application of ES-MDA demonstrating the assimilation of geophysical data Z. Chen et al. 10.1016/j.advwatres.2023.104555
14. Data assimilation for real-time subsurface flow modeling with dynamically adaptive meshless node adjustments S. Chen et al. 10.1007/s00366-023-01897-6
15. An iterative normal-score ensemble smoother for dealing with non-Gaussianity in data assimilation L. Li et al. 10.1016/j.jhydrol.2018.01.038
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18. The importance of state transformations when using the ensemble Kalman filter for unsaturated flow modeling: Dealing with strong nonlinearities D. Erdal et al. 10.1016/j.advwatres.2015.09.008
19. Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter T. Xu & J. Gómez-Hernández 10.1016/j.advwatres.2017.12.011
20. Non-point contaminant source identification in an aquifer using the ensemble smoother with multiple data assimilation T. Xu et al. 10.1016/j.jhydrol.2021.127405
21. Ensemble smoother with multiple data assimilation for reverse flow routing V. Todaro et al. 10.1016/j.cageo.2019.06.002
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23. Posterior population expansion for solving inverse problems C. Jäggli et al. 10.1002/2016WR019550
24. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
25. Joint identification of contaminant source and aquifer geometry in a sandbox experiment with the restart ensemble Kalman filter Z. Chen et al. 10.1016/j.jhydrol.2018.07.073
26. A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty C. Llopis-Albert et al. 10.1016/j.jhydrol.2014.01.021
27. Hybrid Inversion Method to Estimate Hydraulic Transmissivity by Combining Multiple-Point Statistics and a Direct Inversion Method A. Comunian & M. Giudici 10.1007/s11004-018-9727-0
28. Inverse methods in hydrogeology: Evolution and recent trends H. Zhou et al. 10.1016/j.advwatres.2013.10.014
29. Hydrogeochemical characterization and CO2 consumption in the Maqu catchment of the Qinghai-Tibetan Plateau by multiple hydrogeochemical methods M. Li et al. 10.1016/j.jhydrol.2023.129899
30. Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization J. Zhang et al. 10.1029/2020WR027399
31. Gaussian and non‐Gaussian inverse modeling of groundwater flow using copulas and random mixing A. Bárdossy & S. Hörning 10.1002/2014WR016820
32. Multiresolution Approach to Condition Categorical Multiple‐Point Realizations to Dynamic Data With Iterative Ensemble Smoothing D. Lam et al. 10.1029/2019WR025875
33. Identification of non-Gaussian parameters in heterogeneous aquifers by a modified probability conditioning method through hydraulic-head assimilation T. Lan et al. 10.1007/s10040-020-02243-6
34. A new method to improve estimation of uncertain parameters in the Ensemble Kalman filter by re-parameterization employing prior statistics correction A. Bagherinezhad et al. 10.1016/j.jngse.2015.08.057
35. Random Mixing: An Approach to Inverse Modeling for Groundwater Flow and Transport Problems A. Bárdossy & S. Hörning 10.1007/s11242-015-0608-4
36. A comparison of nonlinear extensions to the ensemble Kalman filter I. Grooms 10.1007/s10596-022-10141-x
37. Advances in understanding river‐groundwater interactions P. Brunner et al. 10.1002/2017RG000556
38. Comparing Seven Variants of the Ensemble Kalman Filter: How Many Synthetic Experiments Are Needed? J. Keller et al. 10.1029/2018WR023374
39. Estimation of Facies Boundaries Using Categorical Indicators with P-Field Simulation and Ensemble Kalman Filter (EnKF) S. Nejadi et al. 10.1007/s11053-014-9233-0
40. Maximizing the value of pressure data in saline aquifer characterization S. Yoon et al. 10.1016/j.advwatres.2017.08.019
41. Application of ensemble-based data assimilation techniques for aquifer characterization using tracer data at Hanford 300 area X. Chen et al. 10.1002/2012WR013285
42. Evaluation of the benefits of improved permeability estimation on high-resolution characterization of DNAPL distribution in aquifers with low-permeability lenses Q. Guo et al. 10.1016/j.jhydrol.2021.126955
43. Inverse sequential simulation: A new approach for the characterization of hydraulic conductivities demonstrated on a non‐Gaussian field T. Xu & J. Gómez‐Hernández 10.1002/2014WR016320
44. Characterization of non‐Gaussian conductivities and porosities with hydraulic heads, solute concentrations, and water temperatures T. Xu & J. Gómez‐Hernández 10.1002/2016WR019011
45. Preconditioned Crank‐Nicolson Markov Chain Monte Carlo Coupled With Parallel Tempering: An Efficient Method for Bayesian Inversion of Multi‐Gaussian Log‐Hydraulic Conductivity Fields T. Xu et al. 10.1029/2020WR027110
46. The power of transient piezometric head data in inverse modeling: An application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field T. Xu et al. 10.1016/j.advwatres.2013.01.006