scholarly journals Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter

2018 ◽  
Vol 112 ◽  
pp. 106-123 ◽  
Author(s):  
Teng Xu ◽  
J. Jaime Gómez-Hernández
Keyword(s):  
Kalman Filter ◽  
Hydraulic Conductivity ◽  
Ensemble Kalman Filter ◽  
Contaminant Source ◽  
Normal Score ◽  
Simultaneous Identification

Simultaneous identification of contaminant source location, initial contaminant concentration, horizontal and vertical hydraulic conductivity in a three-dimensional coastal aquifer via ensemble Kalman filter 

2021 ◽  
Author(s):  
Arezou Dodangeh ◽  
Mohammad Mahdi Rajabi ◽  
Marwan Fahs
Keyword(s):  
Kalman Filter ◽  
Hydraulic Conductivity ◽  
Coastal Aquifers ◽  
Ensemble Kalman Filter ◽  
Coastal Aquifer ◽  
Three Dimensional ◽  
Source Location ◽  
Contaminant Concentration ◽  
Contaminant Source ◽  
Vertical Hydraulic Conductivity

<p>In coastal aquifers, we face the problem of salt water intrusion, which creates a complex flow field. Many of these coastal aquifers are also exposed to contaminants from various sources. In addition, in many cases there is no information about the characteristics of the aquifer. Simultaneous identification of the contaminant source and coastal aquifer characteristics can be a challenging issue. Much work has been done to identify the contaminant source, but in the complex velocity field of coastal aquifer, no one has resolved this issue yet. We want to address that in a three-dimensional artificial coastal aquifer.</p><p>To achieve this goal, we have developed a method in which the contaminant source can be identified and the characteristics of the aquifer can be estimated by using information obtained from observation wells. First, by assuming the input parameters required to simulate the contaminant transfer to the aquifer, this three-dimensional coastal aquifer that is affected by various phenomena such as seawater intrusion, tides, shore slope, rain, discharge and injection wells, is simulated and the time series of the output parameters including head, salinity and contaminant concentration are estimated. In the next step, with the aim of performing inverse modeling, random values ​​are added to the time series of outputs obtained at specific points (points belonging to observation wells) in order to rebuilt the initial conditions of the problem to achieve the desired unknowns (contaminant source and aquifer characteristics). The unknowns estimated in this study are the contaminant source location (x, y, z), the initial contaminant concentration, the horizontal and vertical hydraulic conductivity of the aquifer. SEAWAT model in GMS software environment has been used to solve the equations of flow and contaminant transfer and simulate a three-dimensional coastal aquifer. Next, for reverse modeling, one of the Bayesian Filters subset (ensemble Kalman filter) has been used in the Python programming language environment. Also, to reduce the code run time, the neural network model is designed and trained for the SEAWAT model.</p><p>This method is able to meet the main purpose of the study, namely estimating the value ​​of unknown input parameters, including the contaminant source location, the initial contaminant concentration, the horizontal and vertical hydraulic conductivity of the aquifer. In addition, that makes it possible to achieve a three-dimensional numerical model of the coastal aquifer that can be used as a benchmark to examine more accurately the impact of different phenomena simultaneously. In conclusion, we have developed an algorithm which can be used in the world's coastal aquifers to identify the contaminant source and estimate its characteristics.</p><p> </p><p>Key words: coastal aquifer, seawater intrusion, contaminants, groundwater, flow field, parameter estimation, ensemble kalman filter</p>

scholarly journals Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter

2017 ◽  
Vol 548 ◽  
pp. 208-224 ◽  
Author(s):  
Francesco Zovi ◽  
Matteo Camporese ◽  
Harrie-Jan Hendricks Franssen ◽  
Johan Alexander Huisman ◽  
Paolo Salandin
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Multiple Point ◽  
High Permeability ◽  
Subsurface Structures ◽  
Normal Score

scholarly journals Comparison of Two Ensemble-Kalman Filter Based Methods for Estimating Aquifer Parameters from Real 3-D Hydraulic and Tracer Tomographic Tests

Geosciences ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 462
Author(s):  
Emilio Sánchez-León ◽  
Carsten Leven ◽  
Daniel Erdal ◽  
Olaf A. Cirpka
Keyword(s):  
Kalman Filter ◽  
Hydraulic Conductivity ◽  
Ensemble Kalman Filter ◽  
Tracer Tests ◽  
Site Investigation ◽  
Concentration Data ◽  
Arrival Times ◽  
Hydraulic Tomography ◽  
Conservative Tracer ◽  
Observation Wells

Pumping and tracer tests are site-investigation techniques frequently used to determine hydraulic conductivity. Tomographic test layouts, in which multiple tests with different combinations of injection and observation wells are performed, gain a better insight into spatial variability. While hydraulic tomography has repeatedly been applied in the field, tracer tomography lags behind. In a previous publication, we presented a synthetic study to investigate whether the ensemble Kalman Filter (EnKF) or the Kalman Ensemble Generator (KEG) performs better in inverting hydraulic- and tracer-tomographic data. In this work, we develop an experimental method for solute-tracer tomography using fluorescein as a conservative tracer. We performed hydraulic- and tracer-tomographic tests at the Lauswiesen site in Germany. We analyzed transient drawdown and concentration data with the EnKF and steady-state hydraulic heads and mean tracer arrival times with the KEG, obtaining more stable results with the KEG at lower computational costs. The spatial distribution of the estimated hydraulic conductivity field agreed with earlier descriptions of the aquifer at the site. This study narrows the gap between numerical studies and field applications for aquifer characterization at high resolution, showing the potential of combining ensemble-Kalman filter based methods with data collected from hydraulic and solute-tracer tomographic experiments.

scholarly journals Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter

2011 ◽  
Vol 44 (2) ◽  
pp. 169-185 ◽  
Author(s):  
Haiyan Zhou ◽  
Liangping Li ◽  
Harrie-Jan Hendricks Franssen ◽  
J. Jaime Gómez-Hernández
Keyword(s):  
Pattern Recognition ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Normal Score

scholarly journals Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter

2011 ◽  
Vol 8 (4) ◽  
pp. 6749-6788 ◽  
Author(s):  
L. Li ◽  
H. Zhou ◽  
H. J. Hendricks Franssen ◽  
J. J. Gómez-Hernández
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Random Function ◽  
Bimodal Distribution ◽  
Function Model ◽  
Normal Score ◽  
Mean And Variance ◽  
Assimilation Process ◽  
Flow Configurations ◽  
The Individual

Abstract. The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i) the localized NS-EnKF can identify correctly the channels when a large number of conditioning piezometers are used even when an erroneous prior random function model is used, (ii) localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii) the NS-EnKF works equally well under very different flow configurations.

scholarly journals Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter

2016 ◽  
Vol 20 (1) ◽  
pp. 555-569 ◽  
Author(s):  
D. Erdal ◽  
O. A. Cirpka
Keyword(s):  
Kalman Filter ◽  
Hydraulic Conductivity ◽  
Groundwater Recharge ◽  
Prior Knowledge ◽  
Ensemble Kalman Filter ◽  
Joint Estimation ◽  
Inversion Method ◽  
Estimation Of Parameters ◽  
Worst Case ◽  
Joint Inference

Abstract. Regional groundwater flow strongly depends on groundwater recharge and hydraulic conductivity. Both are spatially variable fields, and their estimation is an ongoing topic in groundwater research and practice. In this study, we use the ensemble Kalman filter as an inversion method to jointly estimate spatially variable recharge and conductivity fields from head observations. The success of the approach strongly depends on the assumed prior knowledge. If the structural assumptions underlying the initial ensemble of the parameter fields are correct, both estimated fields resemble the true ones. However, erroneous prior knowledge may not be corrected by the head data. In the worst case, the estimated recharge field resembles the true conductivity field, resulting in a model that meets the observations but has very poor predictive power. The study exemplifies the importance of prior knowledge in the joint estimation of parameters from ambiguous measurements.

scholarly journals Characterizing Curvilinear Features Using the Localized Normal-Score Ensemble Kalman Filter

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Haiyan Zhou ◽  
Liangping Li ◽  
J. Jaime Gómez-Hernández
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Transient State ◽  
Observation Data ◽  
Model Structure ◽  
Prior Model ◽  
State Observation ◽  
Normal Score ◽  
Observation Network

The localized normal-score ensemble Kalman filter is shown to work for the characterization of non-multi-Gaussian distributed hydraulic conductivities by assimilating state observation data. The influence of type of flow regime, number of observation piezometers, and the prior model structure are evaluated in a synthetic aquifer. Steady-state observation data are not sufficient to identify the conductivity channels. Transient-state data are necessary for a good characterization of the hydraulic conductivity curvilinear patterns. Such characterization is very good with a dense network of observation data, and it deteriorates as the number of observation piezometers decreases. It is also remarkable that, even when the prior model structure is wrong, the localized normal-score ensemble Kalman filter can produce acceptable results for a sufficiently dense observation network.

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