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1.
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. 相似文献
2.
岩相和储层物性参数是油藏表征的重要参数,地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论,能够对不同类型的信息源进行综合,建立具有较高分辨率的储层模型,因而得到广泛关注.其中,概率扰动方法是一种高效的迭代随机反演策略,它能综合考虑多种约束信息,且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中,本文有效的联合多点地质统计学与序贯高斯模拟,并结合统计岩石物理理论实现随机反演.首先,通过多点地质统计学随机模拟,获得一系列等可能的岩相模型,扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型;然后通过统计岩石物理理论,计算相应的弹性参数;最后,正演得到合成地震记录并与实际地震数据对比,通过概率扰动方法进行迭代,直到获得满足给定误差要求的反演结果.利用多点地质统计学,能够更好地表征储层空间特征.相控序贯高斯模拟的应用,能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果,模型测试和实际数据应用验证了方法的可行性和有效性. 相似文献
3.
A calibration method to solve the groundwater inverse problem under steady- and transient-state conditions is presented. The method compares kriged and numerical head field gradients to modify hydraulic conductivity without the use of non-linear optimization techniques. The process is repeated iteratively until a close match with piezometric data is reached. The approach includes a damping factor to avoid divergence and oscillation of the solution in areas of low hydraulic gradient and a weighting factor to account for temporal head variation in transient simulations. The efficiency of the method in terms of computing time and calibration results is demonstrated with a synthetic field. It is shown that the proposed method provides parameter fields that reproduce both hydraulic conductivity and piezometric data in few forward model solutions. Stochastic numerical experiments are conducted to evaluate the sensitivity of the method to the damping function and to the head field estimation errors. 相似文献
4.
A covariance-based model-fitting approach is often considered valid to represent field spatial variability of hydraulic properties. This study examines the representation of geologic heterogeneity in two types of geostatistical models under the same mean and spatial covariance structure, and subsequently its effect on the hydraulic response to a pumping test based on 3D high-resolution numerical simulation and field data. Two geostatistical simulation methods, sequential Gaussian simulation (SGS) and transition probability indicator simulation (TPROGS) were applied to create conditional realizations of alluvial fan aquifer systems in the Lawrence Livermore National Laboratory (LLNL) area. The simulated K fields were then used in a numerical groundwater flow model to simulate a pumping test performed at the LLNL site. Spatial connectivity measures of high-K materials (channel facies) captured connectivity characteristics of each geostatistical model and revealed that the TPROGS model created an aquifer (channel) network having greater lateral connectivity. SGS realizations neglected important geologic structures associated with channel and overbank (levee) facies, even though the covariance model used to create these realizations provided excellent fits to sample covariances computed from exhaustive samplings of TPROGS realizations. Observed drawdown response in monitoring wells during a pumping test and its numerical simulation shows that in an aquifer system with strongly connected network of high-K materials, the Gaussian approach could not reproduce a similar behavior in simulated drawdown response found in TPROGS case. Overall, the simulated drawdown responses demonstrate significant disagreement between TPROGS and SGS realizations. This study showed that important geologic characteristics may not be captured by a spatial covariance model, even if that model is exhaustively determined and closely fits the exponential function. 相似文献
5.
In most groundwater applications, measurements of concentration are limited in number and sparsely distributed within the domain of interest. Therefore, interpolation techniques are needed to obtain most likely values of concentration at locations where no measurements are available. For further processing, for example, in environmental risk analysis, interpolated values should be given with uncertainty bounds, so that a geostatistical framework is preferable. Linear interpolation of steady-state concentration measurements is problematic because the dependence of concentration on the primary uncertain material property, the hydraulic conductivity field, is highly nonlinear, suggesting that the statistical interrelationship between concentration values at different points is also nonlinear. We suggest interpolating steady-state concentration measurements by conditioning an ensemble of the underlying log-conductivity field on the available hydrological data in a conditional Monte Carlo approach. Flow and transport simulations for each conditional conductivity field must meet the measurements within their given uncertainty. The ensemble of transport simulations based on the conditional log-conductivity fields yields conditional statistical distributions of concentration at points between observation points. This method implicitly meets physical bounds of concentration values and non-Gaussianity of their statistical distributions and obeys the nonlinearity of the underlying processes. We validate our method by artificial test cases and compare the results to kriging estimates assuming different conditional statistical distributions of concentration. Assuming a beta distribution in kriging leads to estimates of concentration with zero probability of concentrations below zero or above the maximal possible value; however, the concentrations are not forced to meet the advection-dispersion equation. 相似文献
6.
《Advances in water resources》1999,23(1):1-13
The self-calibrated method has been extended for the generation of equally likely realizations of transmissivity and storativity conditional to transmissivity and storativity data and to steady-state and transient hydraulic head data. Conditioning to transmissivity and storativity data is achieved by means of standard geostatistical co-simulation algorithms, whereas conditioning to hydraulic head data, given its non-linear relation to transmissivity and storativity, is achieved through non-linear optimization, similar to standard inverse algorithms. The algorithm is demonstrated in a synthetic study based on data from the WIPP site in New Mexico. Seven alternative scenarios are investigated, generating 100 realizations for each of them. The differences among the scenarios range from the number of conditioning data, to their spatial configuration, to the pumping strategies at the pumping wells. In all scenarios, the self-calibrated algorithm is able to generate transmissivity–storativity realization couples conditional to all the sample data. For the specific case studied here the results are not surprising. Of the piezometric head data, the steady-state values are the most consequential for transmissivity characterization. Conditioning to transient head data only introduces local adjustments on the transmissivity fields and serves to improve the characterization of the storativity fields. 相似文献
7.
MODFLOW 2000 head uncertainty,a first-order second moment method 总被引：1，自引：0，他引：1
A computationally efficient method to estimate the variance and covariance in piezometric head results computed through MODFLOW 2000 using a first-order second moment (FOSM) approach is presented. This methodology employs a first-order Taylor series expansion to combine model sensitivity with uncertainty in geologic data. MODFLOW 2000 is used to calculate both the ground water head and the sensitivity of head to changes in input data. From a limited number of samples, geologic data are extrapolated and their associated uncertainties are computed through a conditional probability calculation. Combining the spatially related sensitivity and input uncertainty produces the variance-covariance matrix, the diagonal of which is used to yield the standard deviation in MODFLOW 2000 head. The variance in piezometric head can be used for calibrating the model, estimating confidence intervals, directing exploration, and evaluating the reliability of a design. A case study illustrates the approach, where aquifer transmissivity is the spatially related uncertain geologic input data. The FOSM methodology is shown to be applicable for calculating output uncertainty for (1) spatially related input and output data, and (2) multiple input parameters (transmissivity and recharge). 相似文献
8.
Hydraulic conductivity identification remains a challenging inverse problem in ground water modeling because of the inherent nonuniqueness and lack of flexibility in parameterization methods. This study introduces maximum weighted log-likelihood estimation (MWLLE) along with multiple generalized parameterization (GP) methods to identify hydraulic conductivity and to address nonuniqueness and inflexibility problems in parameterization. A scaling factor for information criteria is suggested to obtain reasonable weights of parameterization methods for the MWLLE and model averaging method. The scaling factor is a statistical parameter relating to a desired significance level in Occam's window and the variance of the chi-squares distribution of the fitting error. Through model averaging with multiple GP methods, the conditional estimate of hydraulic conductivity and its total conditional covariances are calculated. A numerical example illustrates the issue arising from Occam's window in estimating model weights and shows the usefulness of the scaling factor to obtain reasonable model weights. Moreover, the numerical example demonstrates the advantage of using multiple GP methods over the zonation and interpolation methods because GP provides better models in the model averaging method. The methodology is applied to the Alamitos Gap area, California, to identify the hydraulic conductivity field. The results show that the use of the scaling factor is necessary in order to incorporate good parameterization methods and to avoid a dominant parameterization method. 相似文献
9.
Reliable records of water use for irrigation are often lacking. This presents a difficulty for a qualified water use and water availability assessment. Quantification of the hydrologic cycle processes in regions of intensive agricultural practice requires irrigation as an input to hydrologic models. This paper presents a coupled forward-inverse framework to estimate irrigation schedule using remote-sensed data and data assimilation and optimization techniques. Irrigation schedule is treated as an unknown input to a hydro-agronomic simulation model. Remote-sensed data is used to assess actual crop evapotranspiration, which is used as the “observation” of the computed crop evapotranspiration from the simulation model. To handle the impact of model and observation error and the unknown biased error with irrigation inputs, a coupled forward-inverse approach is proposed, implemented and tested. The coupled approach is realized by an integrated ensemble Kalman filter (EnKF) and genetic algorithm (GA). The result from a case study demonstrates that the forward and inverse procedures in the coupled framework are complementary to each other. Further analysis is provided on the impact of model and observation errors on the non-uniqueness problem with inverse modeling and on the exactness of irrigation estimates. 相似文献
10.
In geostatistical inverse modeling, hydrogeological parameters, such as hydraulic conductivity, are estimated as spatial fields. Upon discretization this results in several thousand (log-)hydraulic conductivity values to be estimated. Common inversion schemes rely on gradient-based parameter estimation methods which require the sensitivity of all measurements with respect to all parameters. Point-like measurements of steady-state concentration in aquifers are generally not well suited for gradient-based methods, because typical plumes exhibit only a very narrow fringe at which the concentration decreases from a maximal value to zero. Only here the sensitivity of concentration with respect to hydraulic conductivity significantly differs from zero. Thus, if point-like measurements of steady-state concentration do not lie in this narrow fringe, their sensitivity with respect to hydraulic conductivity is zero. Observations of concentrations averaged over a larger control volume, by contrast, show a more regular sensitivity pattern. We thus suggest artificially increasing the sampling volume of steady-state concentration measurements for the evaluation of sensitivities in early stages of an iterative parameter estimation scheme. We present criteria for the extent of artificially increasing the sampling volume and for decreasing it when the simulation results converge to the measurements. By this procedure, we achieve high stability in geostatistical inversion of steady-state concentration measurements. The uncertainty of the estimated parameter fields is evaluated by generating conditional realizations. 相似文献
11.
Andrea Zanini Peter K. Kitanidis 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(5):565-577
The estimation of field parameters, such as transmissivity, is an important part of groundwater modeling. This work deals
with the quasilinear geostatistical inverse approach to the estimation of the transmissivity fields from hydraulic head measurements.
The standard quasilinear approach is an iterative method consisting of successive linearizations. We examine a synthetic case
to evaluate the basic methodology and some modifications and extensions. The first objective is to evaluate the performance
of the quasilinear approach when applied to strongly heterogeneous (or “high-contrast”) transmissivity fields and, when needed,
to propose improvements that allow the solution of such problems. For large-contrast cases, the standard quasilinear method
often fails to converge. However, by introducing a derivative-free line search as a polishing step after each Gauss–Newton
iteration, we have found that convergence can be practically assured. Another issue is that the quasilinear procedure, which
uses linearization about the best estimate to evaluate estimation variances, may lead to inaccurate estimation of the variance
of the estimated variable. Our numerical results suggest that this may not be a particularly serious problem, though it is
hard to say whether this conclusion will apply to other cases. Nevertheless, since the quasilinear approach is an approximation,
we propose a potentially more accurate but computer-intensive Markov Chain Monte Carlo (MCMC) procedure based on conditional
realizations generated through the quasilinear approach and accepted or rejected according to the Metropolis–Hastings algorithm.
Six transmissivity fields with increasing contrast were generated and one thousand conditional realizations were computed
for each studied case. The MCMC procedure proposed in this work gives an overall more accurate picture than the quasilinear
approach but at a considerably higher computational cost. 相似文献
12.
Conditional component random fields (CC) based on Cholesky decomposition of the multivariate spectra are introduced in this study to develop a new method for conditional simulation of vector attributes in environmental and geological phenomena. The CC are independent random fields with covariance models obtained from projections and conditioning in the frequency domain. The approach is to simulate one attribute in the physical space and use the results to estimate the other attributes in the frequency domain. Then, a CC for the next attribute is simulated and projected on the other attributes. In general, any attribute is built as the sum of inverse Fourier transform of the orthogonal projection of previous simulated CC plus a last CC simulated in the physical space. This simulation approach continues in this fashion for several attributes and the order of them may be changed for different realizations. This method allows for data conditioning and simulation. A simplified version for intrinsically correlated random fields allows for an approach that avoids the frequency domain. 相似文献
13.
José E. Capilla J. Rodrigo J. J. Gómez-Hernández 《Stochastic Environmental Research and Risk Assessment (SERRA)》1998,12(3):171-190
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. 相似文献
14.
José E. Capilla J. Rodrigo J. J. Gómez-Hernández 《Stochastic Hydrology and Hydraulics》1998,12(3):171-190
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. 相似文献
15.
Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data 总被引：1，自引：1，他引：0
Juxiu Tong Bill X. Hu Jinzhong Yang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(8):1211-1223
A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient
pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity
and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess
the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from
observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly
improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the
hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation
steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity
field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or
larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic
conductivity field calibration. 相似文献
16.
17.
Numerical approximation of head and flux covariances in three dimensions using mixed finite elements
《Advances in water resources》1999,22(7):729-740
A numerical method is developed for accurately approximating head and flux covariances and cross-covariances in finite two- and three-dimensional domains using the mixed finite element method. The method is useful for determining head and flux covariances for non-stationary flow fields, for example those induced by injection or extraction wells, impermeable subsurface barriers, or non-stationary hydraulic conductivity fields. Because the numerical approximations to the flux covariances are obtained directly from the solution to the coupled problem rather than having to differentiate head covariances, the approximations are in general more accurate than those obtained from conventional finite difference or finite element methods. Results for uniform flow example problems are consistent with results from previously published finite domain analyses and demonstrate that head variances and covariances are quite sensitive to boundary conditions and the size of the bounded domain. Flux variances and covariances are less sensitive to boundary conditions and domain size. Results comparing approximations from lower-order Raviart–Thomas–Nedelec and higher order Brezzi–Douglas–Marini[9] finite element spaces indicate that higher order element space improve the estimate of the flux covariances, but do not significantly affect the estimate of the head covariances. 相似文献
18.
ABSTRACT The current state of kriging in subsurface hydrology is critically reviewed. In an application to a region where boreholes already exist, methods of optimal location of additional observation wells for geophysical parameter investigation and optimal interpolation for the purpose of solving the inverse problem are investigated. The particular case of the location of wells for the measurements of transmissivity and hydraulic head in the Kennet Valley Chalk aquifer, UK, is examined. Results of interpolation of measured hydraulic conductivity values by kriging are compared with results from a standard graphical package for interpolation. Reference is also made to the distribution obtained by the inverse method (in which the conductivity distribution is obtained from the head distribution). On the basis of the application, the conditional simulation (in which the generated data are both consistent with field values and the field statistical structure) is deemed to be the best. It is also found that different methods of interpolation give widely different distributions in the case of hydraulic conductivity. It is suggested that the kriged map or conditional map of the transmissivity should serve as the basis for regional discretization to which corrections via the inverse model may be made. 相似文献
19.
Estimating threshold-exceeding probability maps of environmental variables with Markov chain random fields 总被引：2，自引：2，他引：0
Weidong Li Chuanrong Zhang Dipak K. Dey Shanqin Wang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(8):1113-1126
Estimating and mapping spatial uncertainty of environmental variables is crucial for environmental evaluation and decision
making. For a continuous spatial variable, estimation of spatial uncertainty may be conducted in the form of estimating the
probability of (not) exceeding a threshold value. In this paper, we introduced a Markov chain geostatistical approach for
estimating threshold-exceeding probabilities. The differences of this approach compared to the conventional indicator approach
lie with its nonlinear estimators—Markov chain random field models and its incorporation of interclass dependencies through
transiograms. We estimated threshold-exceeding probability maps of clay layer thickness through simulation (i.e., using a
number of realizations simulated by Markov chain sequential simulation) and interpolation (i.e., direct conditional probability
estimation using only the indicator values of sample data), respectively. To evaluate the approach, we also estimated those
probability maps using sequential indicator simulation and indicator kriging interpolation. Our results show that (i) the
Markov chain approach provides an effective alternative for spatial uncertainty assessment of environmental spatial variables
and the probability maps from this approach are more reasonable than those from conventional indicator geostatistics, and
(ii) the probability maps estimated through sequential simulation are more realistic than those through interpolation because
the latter display some uneven transitions caused by spatial structures of the sample data. 相似文献
20.
This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献