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1.
含水层非均质性的刻画是模拟地下水中污染物运移的关键。以渗透系数为研究对象,构建了综合集合卡尔曼滤波方法、有效电阻率模型与地下水运移模型的同化框架,通过融合地球物理观测数据与污染物浓度观测数据来推估渗透系数的空间分布。基于理想算例,验证了该同化框架刻画含水层非均质渗透系数场的有效性,并针对不同初始参数信息与观测类型对比了耦合与非耦合水文地球物理方法的适用性。研究结果表明:基于集合卡尔曼滤波方法同化多种类型的观测数据,可有效地推估非均质参数空间分布。当初始信息较准确时,耦合方法的参数推估精度更高;初始信息存在偏差时,非耦合方法有更好的同化效果。由于非耦合方法计算成本较低且对初始信息缺失时适用性更强,在实际应用中可先基于非耦合方法初步估计参数,再利用耦合方法进一步提高参数推估精度。融合多种类型观测数据可有效提高参数推估效果。 相似文献
2.
重非水相污染物(DNAPL)在地下介质中运移和分布受多种因素控制,包括DNAPL本身的物理化学性质,土的性质,泄漏条件等等。由于介质的非均质性,使得多相流运移行为更为复杂。基于地下水随机理论构建渗透率随机场,采用蒙特卡罗方法探讨泄漏速率对非均质饱和介质中DNAPL运移的影响。数值结果表明,在泄漏总量一定的情况下,泄漏速率越低,介质非均质性对DNAPL运移的影响程度越高。反之,DNAPL的渗漏速率越高,小尺度地层的非均质性影响越低。由于DNAPL运移过程中在垂直方向受重力的影响,污染羽在空间上的质心位置(一阶矩)以及展布范围(二阶矩)在垂直方向上的变异程度要高于水平方向。 相似文献
3.
地下水反应运移模型具有参数个数众多,观测数据类型多样的特点。为了探究不同类型观测数据在反应运移模拟数据同化中的数据价值,构建了三氯乙烯降解反应运移模型的理想算例,基于水头和浓度两种类型观测数据,采用集合卡尔曼滤波方法推估渗透系数和贮水系数的非均质空间分布,讨论了影响同化结果的因素。结果表明:与仅同化水头数据的结果相比,联合同化水头和浓度观测数据推估渗透系数场和贮水系数场时具有更高的精度,在观测数据拟合和模型预测方面也有更好的表现。与目前溶质运移模型、非饱和流模型等地下水模型中的研究结果相似,数据同化结果受样本数量,观测井的数量和位置的影响,合理优化布置监测井和选择样本数量可有效改善数据同化效果并提高计算效率。 相似文献
4.
在冲积含水层中,由于岩相的非均质分布,渗透系数一般呈现出明显的非高斯特性（例如砂和黏土两种岩相）,非高斯特性给地下水模型参数的推估带来了困难与挑战。目前广泛使用的集合平滑数据同化方法（ESMDA）虽然有效且计算成本较低,但仅适用于高斯场。多点地质统计方法虽已广泛用于模拟非高斯场,但其无法融入动态观测数据推估参数。基于多点地质统计方法中的直接采样法（DS）与集合平滑数据同化方法,构建一种新的数据同化框架（ESMDA-DS）,既可保持参数场的非高斯特性,又可融合多源数据精确推估非高斯场。构建三个理想算例验证ESMDA-DS对非高斯参数场的推估效果,并探讨了不同类型观测数据对推估效果、水位与浓度预测精度的影响。三个理想算例包括仅融合水位数据（Case 1）,同时融合水位与浓度数据（Case 2）,同时融合水位、浓度与对数渗透系数数据（Case 3）。结果表明:ESMDA-DS方法结合了ESMDA与DS的各自优势,能有效融合观测数据推估渗透系数场,并保持参数场的非高斯特性。通过对比三个算例推估结果,Case 3的参数场推估效果最好,水位与浓度预测精度最高,Case 2次之,Case 1最差,表明融合多源数据可改善推估效果,提高预测精度。 相似文献
5.
局域化改进集合卡尔曼滤波（EnKF）可以克服EnKF方法在使用小集合时,对参数识别精度较低的缺陷,其能同化
地下水位观测数据有效识别渗透系数场。实际工作中,溶质运移数据也较容易获得。崔凯鹏（2013）尝试增加溶质运移
数据以改进只同化水流数据对渗透系数的估计结果,但是精度提高有限。本文在其基础上修改模型,进一步增加溶质注
入井,探究同时同化水头和溶质运移数据,对渗透系数场识别效果,之后对比了局域化EnKF与非局域化EnKF参数识别结
果,并分析了溶质影响范围与参数识别的关系。结果表明：同时同化溶质运移和水头资料,比同化单一种类观测数据识别
的渗透系数精度更高;相同实现数目下,局域化EnKF比EnKF对渗透系数场的估计结果与真实场更为接近;仅考虑溶质影
响范围内的渗透系数,同化水头数据在最后时刻参数识别结果好于同化溶质运移数据参数识别结果,但差别不大。 相似文献
6.
非均质介质的空间维度变化对重非水相流体（DNAPL） 的运移具有重要的影响。在充分考虑地质体的空间连续
性、不对称性以及各向异性等特征的基础上,采用基于马尔可夫链的转移概率（transition probability） 模型来构建非均质
随机场。该文通过TMVOC-MP软件来模拟DNAPL在非均质介质中的运移规律,探讨非均质随机场的水平空间连续性、
空间维度变化以及侧向运移过程对DNAPL运移的影响。结果表明,介质的水平空间连续性越好,DNAPL在水平方向的
迁移范围越大,在垂向的迁移范围越小;相比于三维模型,二维模型中DNAPL在水平方向的展布更大、在透镜体上的蓄
积量更多,在实际应用中以二维模型代替三维模型会加大模拟结果与实际污染情况之间的误差;侧向运移过程削弱了单
个平面的非均质性对DNAPL运移的控制,当存在侧向运移时,DNAPL绕过透镜体所运移的距离以及在透镜体上的蓄积
量会相应减小。 相似文献
7.
地下水中非水相液体(Non-aqueous Phase Liquid,NAPL)的流动及其曝气修复技术是典型的两相流问题。基于实际地下水含水介质的普遍非均质性,本文应用光透法对非均质孔隙介质中两相流进行了定量试验研究,设计了两组砂箱实验,研究气体和重非水相液体(DNAPL)在非均质孔隙介质中的迁移规律,应用了水/气两相和水/NAPL两相饱和度计算模型。实验结果表明:气体主要由不规则通道向上运动,遇到低渗透性透镜体时在其下方堆积,并开始横向运动,绕过透镜体后继续向上运动,最终在砂箱顶部形成连续气体分布,注气速度越大,气体运移范围越宽;DNAPL在自身重力作用下克服毛管压力向下迁移至低渗透性透镜体,DNAPL无法克服该介质的毛管压力,停止垂向入渗,并在其表面堆积,开始横向运移,绕过透镜体后继续向下运动,最终在砂箱底部形成连续DNAPL污染池。均质介质中建立的计算流体饱和度的水/气模型及水/NAPL模型与实验结果较吻合,可用于非均质多孔介质中水/气相和水/NAPL相饱和度的计算。 相似文献
8.
重非水相（DNAPLs）是地下水常见的有机污染物,理解其运移机制对于污染物修复具有重要意义。为解释实验中多孔介质润湿性对DNAPL的流动速度及残余饱和度的影响,使用多相流相场法,在构造的二维孔隙中模拟DNAPL液滴的下落过程。结果表明：模拟能准确刻画多相流界面的不稳定性,重现与实验室尺度相似的现象。文章提出的双界面模型相较于以往的微观模型能够更好的解释介质润湿性对DNAPL运移的影响。DNAPL的下落速度受前后界面共同影响,而饱和度主要由后界面决定。同时,润湿性对运移的影响大小受制于多孔介质的非均质性。 相似文献
9.
由于裂隙介质具有强烈非均质性,使得重非水相流体(DNAPLs)在裂隙介质中的运移行为较孔隙介质更为复杂.基于指示模拟算法模拟裂隙介质的二元变量(粗糙面接触和裂隙开口),综合模拟退火算法构建裂隙渗透率随机场.采用T2VOC模拟DNAPLs在裂隙介质的运移,探讨粗糙面接触的相关长度、各向异性比、倾角以及非均质性程度对DNAPLs运移分布的影响.数值分析结果表明,粗糙面接触的相关性越差,DNAPLs污染范围越大;粗糙面接触的各向异性比和倾角增大,将导致更多DNAPLs残留;而相关长度和各向异性比增大以及倾角减小,都会引起DNAPLs锋面运移速率增大;随着渗透率非均质性增强,会导致局部粗糙面接触上蓄积的DNAPLs饱和度增大,运移速率减小. 相似文献
10.
目前,刻画场地重非水相液体（dense non-aqueous phase liquid, DNAPL）污染常用的钻孔取样和井间分溶示踪试验方法成本高昂。相比而言,单井注抽试验节省经费,且对污染源区的扰动少,但该试验方法推估DNAPL残留量的准确性尚未得到定量验证。针对该问题,基于数值方法分析了示踪剂类型、注抽速率、污染源区结构等因素对单井注抽试验推估DNAPL残留量精度的影响。结果表明：（1）选用分溶系数比2,2-二甲基-3-戊醇（2,2-dimethyl-3-pentanol, DMP）低的己醇进行示踪,示踪剂回收更加充分,推估污染物残留量的平均精度增幅可达35.11%;(2)当注入速率从100 m3/d提高至130 m3/d、抽出速率从120 m3/d提高至150 m3/d,示踪剂接触的污染源区面积更大,均质源区对应的污染物残留量平均精度从42.45%提高到60.26%,非均质源区对应的平均精度从27.69%提高至48.72%;(3)污染源区结构复杂程度的增加会阻碍示踪剂的运移,非均质源区对应的平均精度比均质源区降低了13.15%;(4)单井注抽示踪试验更适用于离散状为主的污染源... 相似文献
11.
The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale
problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly
difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g.,
water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a
limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the
model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application
of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares
data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the
data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state
space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance
with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with
observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with
second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root
filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem
is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range
by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model
dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem
in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving
ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the
ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance
from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble
methods when applied to nonlinear problems. 相似文献
12.
集合卡尔曼滤波(Ensemble Kalman Filter,EnKF)作为一种有效的数据同化方法,在众多数值实验中体现优势的同时,也暴露了它使用小集合估计协方差情况下精度较低的缺陷。为了降低取样噪声对协方差估计的干扰并提高滤波精度,应用局域化函数对小集合估计的协方差进行修正,即在协方差矩阵中以舒尔积的形式增加空间距离权重以限制远距离相关。在一个二维理想孔隙承压含水层模型中的运行结果表明,局域化对集合卡尔曼滤波估计地下水参数的修正十分有效,局域化可以很好地过滤小集合估计中噪声的影响,节省计算量的同时又可以防止滤波发散。相关长度较小的水文地质参数(如对数渗透系数)更容易受到噪声的干扰,更有必要进行局域化修正。 相似文献
13.
The ensemble Kalman filter (EnKF), an efficient data assimilation method showing advantages in many numerical experiments, is deficient when used in approximating covariance from an ensemble of small size. Implicit localization is used to add distance-related weight to covariance and filter spurious correlations which weaken the EnKF??s capability to estimate uncertainty correctly. The effect of this kind of localization is studied in two-dimensional (2D) and three-dimensional (3D) synthetic cases. It is found that EnKF with localization can capture reliably both the mean and variance of the hydraulic conductivity field with higher efficiency; it can also greatly stabilize the assimilation process as a small-size ensemble is used. Sensitivity experiments are conducted to explore the effect of localization function format and filter lengths. It is suggested that too long or too short filter lengths will prevent implicit localization from modifying the covariance appropriately. Steep localization functions will greatly disturb local dynamics like the 0-1 function even if the function is continuous; four relatively gentle localization functions succeed in avoiding obvious disturbance to the system and improve estimation. As the degree of localization of the L function increases, the parameter sensitivity becomes weak, making parameter selection easier, but more information may be lost in the assimilation process. 相似文献
14.
为研究观测资料稀少情况下土壤质地及有机质对土壤水分同化的影响,发展了集合卡尔曼平滑(Ensemble Kalman Smooth, EnKS)的土壤水分同化方案。利用黑河上游阿柔冻融观测站2008年6月1日至10月29日的观测数据,使用EnKS算法将表层土壤水分观测数据同化到简单生物圈模型(Simple Biosphere Model 2, SiB2)中,分析不同方案对土壤水分估计的影响,并与集合卡尔曼滤波算法(EnKF)的结果进行比较。研究结果表明,土壤质地和有机质对表层土壤水分模拟结果影响最大而对深层的影响相对较小;利用EnKF和EnKS算法同化表层土壤水分观测数据,均能够显著提高表层和根区土壤水分估计的精度,EnKS算法的精度略高于EnKF且所受土壤质地和有机质的影响小于EnKF;当观测数据稀少时,EnKS算法仍然可以得到较高精度的土壤水分估计。 相似文献
15.
利用1998年7月6日至8月9日青藏高原GAME-Tibet试验区MS3608站点的4cm、20cm和100cm的土壤水分观测数据同化SiB2模型输出的表层、根区和深层土壤水分,探讨了一个基于集合卡尔曼滤波和简单生物圈模型的单点土壤水分同化方案。分析和评价了集合大小、同化周期、模型误差、背景场误差以及观测误差对同化系统性能的影响。结果表明:①增加集合数目可以减小土壤水分同化系统的误差,但同时又降低了运行效率;②对于集合卡尔曼滤波,初始场的估计是否准确对同化系统性能影响不大;③模型误差和观测误差的准确估计可以提高土壤水分的估计精度;④利用数据同化的方法对土壤水分的估计有显著提高。 相似文献
16.
Mohammadali Tarrahi Siavash Hakim Elahi Behnam Jafarpour 《Computational Geosciences》2016,20(5):929-952
Ensemble methods present a practical framework for parameter estimation, performance prediction, and uncertainty quantification in subsurface flow and transport modeling. In particular, the ensemble Kalman filter (EnKF) has received significant attention for its promising performance in calibrating heterogeneous subsurface flow models. Since an ensemble of model realizations is used to compute the statistical moments needed to perform the EnKF updates, large ensemble sizes are needed to provide accurate updates and uncertainty assessment. However, for realistic problems that involve large-scale models with computationally demanding flow simulation runs, the EnKF implementation is limited to small-sized ensembles. As a result, spurious numerical correlations can develop and lead to inaccurate EnKF updates, which tend to underestimate or even eliminate the ensemble spread. Ad hoc practical remedies, such as localization, local analysis, and covariance inflation schemes, have been developed and applied to reduce the effect of sampling errors due to small ensemble sizes. In this paper, a fast linear approximate forecast method is proposed as an alternative approach to enable the use of large ensemble sizes in operational settings to obtain more improved sample statistics and EnKF updates. The proposed method first clusters a large number of initial geologic model realizations into a small number of groups. A representative member from each group is used to run a full forward flow simulation. The flow predictions for the remaining realizations in each group are approximated by a linearization around the full simulation results of the representative model (centroid) of the respective cluster. The linearization can be performed using either adjoint-based or ensemble-based gradients. Results from several numerical experiments with two-phase and three-phase flow systems in this paper suggest that the proposed method can be applied to improve the EnKF performance in large-scale problems where the number of full simulation is constrained. 相似文献
17.
In this paper, a stochastic collocation-based Kalman filter (SCKF) is developed to estimate the hydraulic conductivity from
direct and indirect measurements. It combines the advantages of the ensemble Kalman filter (EnKF) for dynamic data assimilation
and the polynomial chaos expansion (PCE) for efficient uncertainty quantification. In this approach, the random log hydraulic
conductivity field is first parameterized by the Karhunen–Loeve (KL) expansion and the hydraulic pressure is expressed by
the PCE. The coefficients of PCE are solved with a collocation technique. Realizations are constructed by choosing collocation
point sets in the random space. The stochastic collocation method is non-intrusive in that such realizations are solved forward
in time via an existing deterministic solver independently as in the Monte Carlo method. The needed entries of the state covariance
matrix are approximated with the coefficients of PCE, which can be recovered from the collocation results. The system states
are updated by updating the PCE coefficients. A 2D heterogeneous flow example is used to demonstrate the applicability of
the SCKF with respect to different factors, such as initial guess, variance, correlation length, and the number of observations.
The results are compared with those from the EnKF method. It is shown that the SCKF is computationally more efficient than
the EnKF under certain conditions. Each approach has its own advantages and limitations. The performance of the SCKF decreases
with larger variance, smaller correlation ratio, and fewer observations. Hence, the choice between the two methods is problem
dependent. As a non-intrusive method, the SCKF can be easily extended to multiphase flow problems. 相似文献