Abstract
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.
Highlights
Parameter identification is a critical step in constructing a reliable model
We propose the use of the normal-score ensemble Kalman filter NS-EnKF proposed by Zhou et al 10
For hydraulic conductivity identification purposes, steady-state information is clearly insufficient, even, as is the case, a very dense network of observations is used 111 wells as shown in Figure 4 a
Summary
The process of recognizing model parameters is referred to as inverse problem or data assimilation; that is, assimilate the system state data to identify the model parameters. The parameters describing the movement of groundwater can vary over several degrees of magnitude within the same aquifer, and the characterization of their spatial variability is very important. For this reason, inverse modeling has been a subject of active research, aiming to take the maximum advantage of the state observation data for the characterization of model parameters, such as hydraulic conductivities. One of the inverse methods that has attracted more attention in hydrogeology lately is the ensemble Kalman filter on its different implementations
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