The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Andres Sahuquillo - One of the best experts on this subject based on the ideXlab platform.
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Stochastic conditional inverse modeling of subsurface mass transport: A brief review and the self-calibrating method
Stochastic Environmental Research and Risk Assessment (SERRA), 2003Co-Authors: J. Jaime Gómez-hernández, Harrie-jan Hendricks Franssen, Andres SahuquilloAbstract:Conditioning transmissivity realizations to state variable data is complex due to the non-linear dependence of transmissivity (or any univariate transform of it) and Piezometric Heads, concentrations or velocities. A review of the literature shows these complexities. The self-calibrating algorithm combines standard geostatistics and non-linear optimization in a way that allows the generation of multiple realizations of logtransmissivity, which are conditioned not only to logtransmissivity measurements but also to Piezometric Head and concentration data. The self-calibrating method is demonstrated in a two-dimensional synthetic exercise in which the trade-offs between transmissivity, Piezometric Head and concentration data are analyzed.
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Sequential self-calibrating multiple realizations of transmissivity for flow and transport data
IAHS-AISH publication, 2003Co-Authors: J. Jaime Gómez-hernández, Harrie-jan W. M. Hendricks Franssen, Andres SahuquilloAbstract:The self-calibration algorithm for conditioning realizations of transmissivity fields to Piezometric-Head data has been extended for conditioning concentration data. The possibility of sequentially including conditioning data allows evaluation of the reliability of model predictions as a function of the amount and type of information used. In a two-dimensional synthetic exercise we show the trade-off between transmissivity, Piezometric Head and concentration Head data. Each type of data has its own relevance, with the best predictions obtained, as expected, when all data types are used for conditioning.
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stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric Head data 3 application to the culebra formation at the waste isolation pilot plan wipp new mexico usa
Journal of Hydrology, 1998Co-Authors: Jose E Capilla, Jaime J Gomezhernandez, Andres SahuquilloAbstract:The self-calibrated approach is applied to the stochastic analysis of groundwater flow and advective mass transport in the WIPP site. Multiple equally likely realizations of logtransmissivity fields are generated, followed by the solution of variable density groundwater flow and particle tracking. Five different cases have been analyzed. The first one regards the modeling of variable-density groundwater flow and the remaining four regard the generation of the logtransmissivity fields. Results show that (i) it is important to model variable-density flow as accurately as possible, (ii) conditioning to Piezometric Head data helps in reducing the uncertainty in flow and transport predictions, (iii) accounting for uncertainty in boundary conditions helps improving the match to measured Heads, and (iv) the interpreted value at location P-18 is not consistent with the model of spatial variability inferred from the data.
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Stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric Head data—3. Application to the Culebra formation at the Waste Isolation Pilot Plan (WIPP), New Mexico, USA
Journal of Hydrology, 1998Co-Authors: Jose E Capilla, J. Jaime Gómez-hernández, Andres SahuquilloAbstract:The self-calibrated approach is applied to the stochastic analysis of groundwater flow and advective mass transport in the WIPP site. Multiple equally likely realizations of logtransmissivity fields are generated, followed by the solution of variable density groundwater flow and particle tracking. Five different cases have been analyzed. The first one regards the modeling of variable-density groundwater flow and the remaining four regard the generation of the logtransmissivity fields. Results show that (i) it is important to model variable-density flow as accurately as possible, (ii) conditioning to Piezometric Head data helps in reducing the uncertainty in flow and transport predictions, (iii) accounting for uncertainty in boundary conditions helps improving the match to measured Heads, and (iv) the interpreted value at location P-18 is not consistent with the model of spatial variability inferred from the data.
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stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric data 2 demonstration on a synthetic aquifer
Journal of Hydrology, 1997Co-Authors: Jose E Capilla, Jaime J Gomezhernandez, Andres SahuquilloAbstract:In the first paper of this series a methodology for the generation of transmissivity fields conditional to both transmissivity and Piezometric Head data was presented. This methodology, termed the self-calibrated approach, consists of two steps: first, the generation of a seed transmissivity field conditioned only to transmissivity data, and second, the perturbation of the seed field up until the Piezometric Head data are reproduced. The methodology is now demonstrated on a set of controlled numerical experiments carried out on synthetic aquifers. The objective of these experiments is not just to show that the methodology works, but also to explore its robustness under different situations. A total of 12 experiments have analyzed the performance of the method as a function of: (i) the log10 T transmissivity variance (from 0.2 to 2.0); (ii) the number of log10 T conditioning data (from 10 to 30); (iii) the number of Piezometric Head data (from 30 to 90); (iv) the number of master points (from 25 to 1000); (v) the magnitude of allowed departure of the final T field from the seed field (up to four times the kriging standard deviation). In all cases, the method was able to generate transmissivity fields conditional to both transmissivity and Head measurements, at the same time preserving the spatial variability of the transmissivity field. It was found that the performance of the method increases with both the number of log10 T data and the number of master points, whereas it decreases as either the log10 T variance or the number of Piezometric Head data increases.
J. Jaime Gómez-hernández - One of the best experts on this subject based on the ideXlab platform.
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Characterization of non‐Gaussian conductivities and porosities with hydraulic Heads, solute concentrations, and water temperatures
Water Resources Research, 2016Co-Authors: J. Jaime Gómez-hernándezAbstract:Reliable characterization of hydraulic parameters is important for the understanding of groundwater flow and solute transport. The normal-score ensemble Kalman filter (NS-EnKF) has proven to be an effective inverse method for the characterization of non-Gaussian hydraulic conductivities by assimilating transient Piezometric Head data, or solute concentration data. Groundwater temperature, an easily captured state variable, has not drawn much attention as an additional state variable useful for the characterization of aquifer parameters. In this work, we jointly estimate non-Gaussian aquifer parameters (hydraulic conductivities and porosities) by assimilating three kinds of state variables (Piezometric Head, solute concentration, and groundwater temperature) using the NS-EnKF. A synthetic example including seven tests is designed, and used to evaluate the ability to characterize hydraulic conductivity and porosity in a non-Gaussian setting by assimilating different numbers and types of state variables. The results show that characterization of aquifer parameters can be improved by assimilating groundwater temperature data and that the main patters of the non-Gaussian reference fields can be retrieved with more accuracy and higher precision if multiple state variables are assimilated.
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A local-global pattern matching method for subsurface stochastic inverse modeling
Environmental Modelling & Software, 2015Co-Authors: Sanjay Srinivasan, Haiyan Zhou, J. Jaime Gómez-hernándezAbstract:Inverse modeling is an essential step for reliable modeling of subsurface flow and transport, which is important for groundwater resource management and aquifer remediation. Multiple-point statistics (MPS) based reservoir modeling algorithms, beyond traditional two-point statistics-based methods, offer an alternative to simulate complex geological features and patterns, conditioning to observed conductivity data. Parameter estimation, within the framework of MPS, for the characterization of conductivity fields using measured dynamic data such as Piezometric Head data, remains one of the most challenging tasks in geologic modeling. We propose a new local-global pattern matching method to integrate dynamic data into geological models. The local pattern is composed of conductivity and Head values that are sampled from joint training images comprising of geological models and the corresponding simulated Piezometric Heads. Subsequently, a global constraint is enforced on the simulated geologic models in order to match the measured Head data. The method is sequential in time, and as new Piezometric Head become available, the training images are updated for the purpose of reducing the computational cost of pattern matching. As a result, the final suite of models preserve the geologic features as well as match the dynamic data. This local-global pattern matching method is demonstrated for simulating a two-dimensional, bimodally-distributed heterogeneous conductivity field. The results indicate that the characterization of conductivity as well as flow and transport predictions are improved when the Piezometric Head data are integrated into the geological modeling. A local-global pattern matching inverse method is proposed.The connectivity can be preserved through multiple point geostatistics.Static and dynamic data are integrated into the geological modeling.
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When Steady-State Is Not Enough
Lecture Notes in Earth System Sciences, 2013Co-Authors: J. Jaime Gómez-hernández, Haiyan ZhouAbstract:Steady-state Piezometric Head data have always been regarded as containing only information about the major patterns of variability of hydraulic conductivity, but not about specific features, such as channels, or local scale heterogeneity. We have attempted to characterize a channeled aquifer using only steady-state Piezometric Head without success. However, in a companion paper, we demonstrate how transient Piezometric Head data can be used to characterize a bimodal aquifer for which no prior information on the aquifer spatial heterogeneity is available using a localized version of the normal-score ensemble Kalman filter.
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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
Advances in Water Resources, 2013Co-Authors: J. Jaime Gómez-hernández, Haiyan ZhouAbstract:Abstract The localized normal-score ensemble Kalman filter (NS-EnKF) coupled with covariance inflation is used to characterize the spatial variability of a channelized bimodal hydraulic conductivity field, for which the only existing prior information about conductivity is its univariate marginal distribution. We demonstrate that we can retrieve the main patterns of the reference field by assimilating a sufficient number of Piezometric observations using the NS-EnKF. The possibility of characterizing the conductivity spatial variability using only Piezometric Head data shows the importance of accounting for these data in inverse modeling.
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Jointly Mapping Hydraulic Conductivity and Porosity by Assimilating Concentration Data via Ensemble Kalman Filter
Journal of Hydrology, 2012Co-Authors: Haiyan Zhou, J. Jaime Gómez-hernández, Harrie-jan Hendricks FranssenAbstract:Summary Real-time data from on-line sensors offer the possibility to update environmental simulation models in real-time. Information from on-line sensors concerning contaminant concentrations in groundwater allow for the real-time characterization and control of a contaminant plume. In this paper it is proposed to use the CPU-efficient Ensemble Kalman Filter (EnKF) method, a data assimilation algorithm, for jointly updating the flow and transport parameters (hydraulic conductivity and porosity) and state variables (Piezometric Head and concentration) of a groundwater flow and contaminant transport problem. A synthetic experiment is used to demonstrate the capability of the EnKF to estimate hydraulic conductivity and porosity by assimilating dynamic Head and multiple concentration data in a transient flow and transport model. In this work the worth of hydraulic conductivity, porosity, Piezometric Head, and concentration data is analyzed in the context of aquifer characterization and prediction uncertainty reduction. The results indicate that the characterization of the hydraulic conductivity and porosity fields is continuously improved as more data are assimilated. Also, groundwater flow and mass transport predictions are improved as more and different types of data are assimilated. The beneficial impact of accounting for multiple concentration data is patent.
Jose E Capilla - One of the best experts on this subject based on the ideXlab platform.
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Worth of secondary data compared to Piezometric data for the probabilistic assessment of radionuclide migration
Stochastic Hydrology and Hydraulics, 1998Co-Authors: Jose E Capilla, Javier Rodrigo, J. Jaime Gómez-hernándezAbstract:A common approach for the performance assessment of radionuclide migration from a nuclear waste repository is by means of Monte-Carlo techniques. Multiple realizations of the parameters controlling radionuclide transport are generated and each one of these realizations is used in a numerical model to provide a transport prediction. The statistical analysis of all transport predictions is then used in performance assessment. In order to reduce the uncertainty on the predictions is necessary to incorporate as much information as possible in the generation of the parameter fields. In this regard, this paper focuses in the impact that conditioning the transmissivity fields to geophysical data and/or Piezometric Head data has on convective transport predictions in a two-dimensional heterogeneous formation. The Walker Lake data based is used to produce a heterogeneous log-transmissivity field with distinct non-Gaussian characteristics and a secondary variable that represents some geophysical attribute. In addition, the Piezometric Head field resulting from the steady-state solution of the groundwater flow equation is computed. These three reference fields are sampled to mimic a sampling campaign. Then, a series of Monte-Carlo exercises using different combinations of sampled data shows the relative worth of secondary data with respect to Piezometric Head data for transport predictions. The analysis shows that secondary data allows to reproduce the main spatial patterns of the reference transmissivity field and improves the mass transport predictions with respect to the case in which only transmissivity data is used. However, a few Piezometric Head measurements could be equally effective for the characterization of transport predictions.
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stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric Head data 3 application to the culebra formation at the waste isolation pilot plan wipp new mexico usa
Journal of Hydrology, 1998Co-Authors: Jose E Capilla, Jaime J Gomezhernandez, Andres SahuquilloAbstract:The self-calibrated approach is applied to the stochastic analysis of groundwater flow and advective mass transport in the WIPP site. Multiple equally likely realizations of logtransmissivity fields are generated, followed by the solution of variable density groundwater flow and particle tracking. Five different cases have been analyzed. The first one regards the modeling of variable-density groundwater flow and the remaining four regard the generation of the logtransmissivity fields. Results show that (i) it is important to model variable-density flow as accurately as possible, (ii) conditioning to Piezometric Head data helps in reducing the uncertainty in flow and transport predictions, (iii) accounting for uncertainty in boundary conditions helps improving the match to measured Heads, and (iv) the interpreted value at location P-18 is not consistent with the model of spatial variability inferred from the data.
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Stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric Head data—3. Application to the Culebra formation at the Waste Isolation Pilot Plan (WIPP), New Mexico, USA
Journal of Hydrology, 1998Co-Authors: Jose E Capilla, J. Jaime Gómez-hernández, Andres SahuquilloAbstract:The self-calibrated approach is applied to the stochastic analysis of groundwater flow and advective mass transport in the WIPP site. Multiple equally likely realizations of logtransmissivity fields are generated, followed by the solution of variable density groundwater flow and particle tracking. Five different cases have been analyzed. The first one regards the modeling of variable-density groundwater flow and the remaining four regard the generation of the logtransmissivity fields. Results show that (i) it is important to model variable-density flow as accurately as possible, (ii) conditioning to Piezometric Head data helps in reducing the uncertainty in flow and transport predictions, (iii) accounting for uncertainty in boundary conditions helps improving the match to measured Heads, and (iv) the interpreted value at location P-18 is not consistent with the model of spatial variability inferred from the data.
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stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric data 2 demonstration on a synthetic aquifer
Journal of Hydrology, 1997Co-Authors: Jose E Capilla, Jaime J Gomezhernandez, Andres SahuquilloAbstract:In the first paper of this series a methodology for the generation of transmissivity fields conditional to both transmissivity and Piezometric Head data was presented. This methodology, termed the self-calibrated approach, consists of two steps: first, the generation of a seed transmissivity field conditioned only to transmissivity data, and second, the perturbation of the seed field up until the Piezometric Head data are reproduced. The methodology is now demonstrated on a set of controlled numerical experiments carried out on synthetic aquifers. The objective of these experiments is not just to show that the methodology works, but also to explore its robustness under different situations. A total of 12 experiments have analyzed the performance of the method as a function of: (i) the log10 T transmissivity variance (from 0.2 to 2.0); (ii) the number of log10 T conditioning data (from 10 to 30); (iii) the number of Piezometric Head data (from 30 to 90); (iv) the number of master points (from 25 to 1000); (v) the magnitude of allowed departure of the final T field from the seed field (up to four times the kriging standard deviation). In all cases, the method was able to generate transmissivity fields conditional to both transmissivity and Head measurements, at the same time preserving the spatial variability of the transmissivity field. It was found that the performance of the method increases with both the number of log10 T data and the number of master points, whereas it decreases as either the log10 T variance or the number of Piezometric Head data increases.
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significance of conditioning to Piezometric Head data for predictions of mass transport in groundwater modeling
Mathematical Geosciences, 1996Co-Authors: Xianhuan Wen, Jaime J Gomezhernandez, Jose E Capilla, Andres SahuquilloAbstract:Transmissivity and Head data are sampled from an exhaustive synthetic reference field and used to predict the arrival positions and arrival times of a number of particles transported across the field, together with an uncertainty estimate. Different combinations of number of transmissivity data and number of Head data used are considered in each one of a series of 64 Monte-Carlo analyses. In each analysis, 250 realizations of transmissivity fields conditioned to both transmissivity and Head data are generated using a novel geostatistically based inverse method. Pooling the solutions of the flow and transport equations in all 250 realizations allows building conditional frequency distributions for particle arrival positions and arrival times. By comparing these fresquency distributions, we can assess the incremental gain that additional Head data provide. The main conclusion is that the first few Head data dramatically improve the quality of transport predictions.
Haiyan Zhou - One of the best experts on this subject based on the ideXlab platform.
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A local-global pattern matching method for subsurface stochastic inverse modeling
Environmental Modelling & Software, 2015Co-Authors: Sanjay Srinivasan, Haiyan Zhou, J. Jaime Gómez-hernándezAbstract:Inverse modeling is an essential step for reliable modeling of subsurface flow and transport, which is important for groundwater resource management and aquifer remediation. Multiple-point statistics (MPS) based reservoir modeling algorithms, beyond traditional two-point statistics-based methods, offer an alternative to simulate complex geological features and patterns, conditioning to observed conductivity data. Parameter estimation, within the framework of MPS, for the characterization of conductivity fields using measured dynamic data such as Piezometric Head data, remains one of the most challenging tasks in geologic modeling. We propose a new local-global pattern matching method to integrate dynamic data into geological models. The local pattern is composed of conductivity and Head values that are sampled from joint training images comprising of geological models and the corresponding simulated Piezometric Heads. Subsequently, a global constraint is enforced on the simulated geologic models in order to match the measured Head data. The method is sequential in time, and as new Piezometric Head become available, the training images are updated for the purpose of reducing the computational cost of pattern matching. As a result, the final suite of models preserve the geologic features as well as match the dynamic data. This local-global pattern matching method is demonstrated for simulating a two-dimensional, bimodally-distributed heterogeneous conductivity field. The results indicate that the characterization of conductivity as well as flow and transport predictions are improved when the Piezometric Head data are integrated into the geological modeling. A local-global pattern matching inverse method is proposed.The connectivity can be preserved through multiple point geostatistics.Static and dynamic data are integrated into the geological modeling.
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When Steady-State Is Not Enough
Lecture Notes in Earth System Sciences, 2013Co-Authors: J. Jaime Gómez-hernández, Haiyan ZhouAbstract:Steady-state Piezometric Head data have always been regarded as containing only information about the major patterns of variability of hydraulic conductivity, but not about specific features, such as channels, or local scale heterogeneity. We have attempted to characterize a channeled aquifer using only steady-state Piezometric Head without success. However, in a companion paper, we demonstrate how transient Piezometric Head data can be used to characterize a bimodal aquifer for which no prior information on the aquifer spatial heterogeneity is available using a localized version of the normal-score ensemble Kalman filter.
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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
Advances in Water Resources, 2013Co-Authors: J. Jaime Gómez-hernández, Haiyan ZhouAbstract:Abstract The localized normal-score ensemble Kalman filter (NS-EnKF) coupled with covariance inflation is used to characterize the spatial variability of a channelized bimodal hydraulic conductivity field, for which the only existing prior information about conductivity is its univariate marginal distribution. We demonstrate that we can retrieve the main patterns of the reference field by assimilating a sufficient number of Piezometric observations using the NS-EnKF. The possibility of characterizing the conductivity spatial variability using only Piezometric Head data shows the importance of accounting for these data in inverse modeling.
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Jointly Mapping Hydraulic Conductivity and Porosity by Assimilating Concentration Data via Ensemble Kalman Filter
Journal of Hydrology, 2012Co-Authors: Haiyan Zhou, J. Jaime Gómez-hernández, Harrie-jan Hendricks FranssenAbstract:Summary Real-time data from on-line sensors offer the possibility to update environmental simulation models in real-time. Information from on-line sensors concerning contaminant concentrations in groundwater allow for the real-time characterization and control of a contaminant plume. In this paper it is proposed to use the CPU-efficient Ensemble Kalman Filter (EnKF) method, a data assimilation algorithm, for jointly updating the flow and transport parameters (hydraulic conductivity and porosity) and state variables (Piezometric Head and concentration) of a groundwater flow and contaminant transport problem. A synthetic experiment is used to demonstrate the capability of the EnKF to estimate hydraulic conductivity and porosity by assimilating dynamic Head and multiple concentration data in a transient flow and transport model. In this work the worth of hydraulic conductivity, porosity, Piezometric Head, and concentration data is analyzed in the context of aquifer characterization and prediction uncertainty reduction. The results indicate that the characterization of the hydraulic conductivity and porosity fields is continuously improved as more data are assimilated. Also, groundwater flow and mass transport predictions are improved as more and different types of data are assimilated. The beneficial impact of accounting for multiple concentration data is patent.
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Modeling transient groundwater flow by coupling ensemble Kalman filtering and upscaling
Water Resources Research, 2012Co-Authors: Haiyan Zhou, Harrie-jan Hendricks Franssen, J. Jaime Gómez-hernándezAbstract:[1] The ensemble Kalman filter (EnKF) is coupled with upscaling to build an aquifer model at a coarser scale than the scale at which the conditioning data (conductivity and Piezometric Head) had been taken for the purpose of inverse modeling. Building an aquifer model at the support scale of observations is most often impractical since this would imply numerical models with many millions of cells. If, in addition, an uncertainty analysis is required involving some kind of Monte Carlo approach, the task becomes impossible. For this reason, a methodology has been developed that will use the conductivity data at the scale at which they were collected to build a model at a (much) coarser scale suitable for the inverse modeling of groundwater flow and mass transport. It proceeds as follows: (1) Generate an ensemble of realizations of conductivities conditioned to the conductivity data at the same scale at which conductivities were collected. (2) Upscale each realization onto a coarse discretization; on these coarse realizations, conductivities will become tensorial in nature with arbitrary orientations of their principal components. (3) Apply the EnKF to the ensemble of coarse conductivity upscaled realizations in order to condition the realizations to the measured Piezometric Head data. The proposed approach addresses the problem of how to deal with tensorial parameters, at a coarse scale, in ensemble Kalman filtering while maintaining the conditioning to the fine-scale hydraulic conductivity measurements. We demonstrate our approach in the framework of a synthetic worth-of-data exercise, in which the relevance of conditioning to conductivities, Piezometric Heads, or both is analyzed.
Jaime J Gomezhernandez - One of the best experts on this subject based on the ideXlab platform.
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an approach to handling non gaussianity of parameters and state variables in ensemble kalman filtering
Advances in Water Resources, 2011Co-Authors: Jaime J Gomezhernandez, Haiyan Zhou, Hendrikus Johannes Hendricks W FranssenAbstract:Abstract The ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to condition log-conductivity realizations on log-conductivity and transient Piezometric Head data. In this case, the state vector is made up of log-conductivities and Piezometric Heads over a discretized aquifer domain, the forecast model is a groundwater flow numerical model, and the transient Piezometric Head data are sequentially assimilated to update the state vector. It is well known that all Kalman filters perform optimally for linear forecast models and a multiGaussian-distributed state vector. Of the different Kalman filters, the EnKF provides a robust solution to address non–linearities; however, it does not handle well non-Gaussian state-vector distributions. In the standard EnKF, as time passes and more state observations are assimilated, the distributions become closer to Gaussian, even if the initial ones are clearly non-Gaussian. A new method is proposed that transforms the original state vector into a new vector that is univariate Gaussian at all times. Back transforming the vector after the filtering ensures that the initial non-Gaussian univariate distributions of the state-vector components are preserved throughout. The proposed method is based in normal-score transforming each variable for all locations and all time steps. This new method, termed the normal-score ensemble Kalman filter (NS-EnKF), is demonstrated in a synthetic bimodal aquifer resembling a fluvial deposit, and it is compared to the standard EnKF. The proposed method performs better than the standard EnKF in all aspects analyzed (log-conductivity characterization and flow and transport predictions).
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stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric Head data 3 application to the culebra formation at the waste isolation pilot plan wipp new mexico usa
Journal of Hydrology, 1998Co-Authors: Jose E Capilla, Jaime J Gomezhernandez, Andres SahuquilloAbstract:The self-calibrated approach is applied to the stochastic analysis of groundwater flow and advective mass transport in the WIPP site. Multiple equally likely realizations of logtransmissivity fields are generated, followed by the solution of variable density groundwater flow and particle tracking. Five different cases have been analyzed. The first one regards the modeling of variable-density groundwater flow and the remaining four regard the generation of the logtransmissivity fields. Results show that (i) it is important to model variable-density flow as accurately as possible, (ii) conditioning to Piezometric Head data helps in reducing the uncertainty in flow and transport predictions, (iii) accounting for uncertainty in boundary conditions helps improving the match to measured Heads, and (iv) the interpreted value at location P-18 is not consistent with the model of spatial variability inferred from the data.
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stochastic simulation of transmissivity fields conditional to both transmissivity and Piezometric data 2 demonstration on a synthetic aquifer
Journal of Hydrology, 1997Co-Authors: Jose E Capilla, Jaime J Gomezhernandez, Andres SahuquilloAbstract:In the first paper of this series a methodology for the generation of transmissivity fields conditional to both transmissivity and Piezometric Head data was presented. This methodology, termed the self-calibrated approach, consists of two steps: first, the generation of a seed transmissivity field conditioned only to transmissivity data, and second, the perturbation of the seed field up until the Piezometric Head data are reproduced. The methodology is now demonstrated on a set of controlled numerical experiments carried out on synthetic aquifers. The objective of these experiments is not just to show that the methodology works, but also to explore its robustness under different situations. A total of 12 experiments have analyzed the performance of the method as a function of: (i) the log10 T transmissivity variance (from 0.2 to 2.0); (ii) the number of log10 T conditioning data (from 10 to 30); (iii) the number of Piezometric Head data (from 30 to 90); (iv) the number of master points (from 25 to 1000); (v) the magnitude of allowed departure of the final T field from the seed field (up to four times the kriging standard deviation). In all cases, the method was able to generate transmissivity fields conditional to both transmissivity and Head measurements, at the same time preserving the spatial variability of the transmissivity field. It was found that the performance of the method increases with both the number of log10 T data and the number of master points, whereas it decreases as either the log10 T variance or the number of Piezometric Head data increases.
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significance of conditioning to Piezometric Head data for predictions of mass transport in groundwater modeling
Mathematical Geosciences, 1996Co-Authors: Xianhuan Wen, Jaime J Gomezhernandez, Jose E Capilla, Andres SahuquilloAbstract:Transmissivity and Head data are sampled from an exhaustive synthetic reference field and used to predict the arrival positions and arrival times of a number of particles transported across the field, together with an uncertainty estimate. Different combinations of number of transmissivity data and number of Head data used are considered in each one of a series of 64 Monte-Carlo analyses. In each analysis, 250 realizations of transmissivity fields conditioned to both transmissivity and Head data are generated using a novel geostatistically based inverse method. Pooling the solutions of the flow and transport equations in all 250 realizations allows building conditional frequency distributions for particle arrival positions and arrival times. By comparing these fresquency distributions, we can assess the incremental gain that additional Head data provide. The main conclusion is that the first few Head data dramatically improve the quality of transport predictions.