共查询到17条相似文献，搜索用时 187 毫秒
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非线性滤波方法与陆面数据同化 总被引：8，自引：4，他引：4
陆面数据同化研究近几年成为地球科学研究的新兴领域,其中以非线性滤波为代表的数据同化方法发展迅速并得到了广泛应用。在贝叶斯理论框架内,从递推贝叶斯估计理论的角度系统地分析了扩展卡尔曼滤波、无迹卡尔曼滤波、集合卡尔曼滤波、SIR粒子滤波等非线性滤波方法的异同;针对应用比较广泛的集合卡尔曼滤波和SIR粒子滤波应用中存在的问题,论述了几种提高滤波性能的实用方法,如协方差矩阵的Localization方法、协方差矩阵的Inflation方法、双集合卡尔曼滤波方法、扰动集合、扰动大气驱动和模型参数、平方根集合卡尔曼滤波以及粒子滤波算法的改进等。最后总结讨论了各种非线性滤波方法应用中的特点、难点以及各种算法在陆面数据同化中的应用前景和发展方向。 相似文献
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集合卡曼滤波由于易于使用而被广泛地应用到陆面数据同化研究中,它是建立在模型为线性、误差为正态分布的假设上,而实际土壤湿度方程是高度非线性的,并且当土壤过干或过湿时会发生样本偏斜.为了全面评估它在同化表层土壤湿度观测来反演土壤湿度廓线的性能,特引入不需要上述假设的采样重要性重采样粒子滤波,比较非线性和偏斜性对同化算法的影响.结果显示:不管是小样本还是大样本,集合卡曼滤波都能快速、准确地逼近样本均值,而粒子滤波只有在大样本时才能缓慢地趋近;此外,集合卡曼滤波的粒子边缘概率密度及其偏度和峰度与粒子滤波完全不同,前者粒子虽不完全满足正态分布,但始终为单峰状态,而后者粒子随同化推进经历了单峰到双峰再到单峰的变化. 相似文献
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不同滤波算法在土壤湿度同化中的应用 总被引：1，自引：0，他引：1
为研究不同滤波算法在土壤湿度同化中的有效性,以及土壤湿度模拟结果对模型参数的敏感性,结合简单生物圈模型SiB2,设置敏感性实验,探求土壤饱和水力传导度对土壤湿度模拟结果的影响;并在此基础上,采用集合卡尔曼滤波（EnKF）、无迹卡尔曼滤波（UKF）和无迹粒子滤波（UPF）开展土壤湿度实时同化实验。结果表明：土壤饱和水力传导度能显著影响土壤湿度模拟精度;利用EnKF、UKF、UPF同化站点观测数据,均能改善土壤湿度模拟结果;3种同化方法在不同土壤层的同化效果不同,在土壤表层,EnKF的有效性优于UKF和UPF,在根域层和土壤深层,3种滤波方法有效性在降雨前后相差较大。因此,针对性地选择同化方法,是提高土壤湿度模拟精度的有效手段。 相似文献
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集合卡尔曼滤波（Ensemble Kalman Filter,EnKF）方法已广泛应用于地下水水流和污染物运移模拟相关问题的求解。但前人研究多建立在同化系统预报模型是准确的基础上,忽视了模型概化的不确定性。当模型概化不准确时,将导致预报偏差,可能带来错误的系统估计。因此,文章提出考虑模型预报偏差的迭代式集合卡尔曼滤波（Bias aware Ensemble Kalman Filter with Confirming Option,Bias-CEnKF）方法。以地下水水流数据同化为例,研究模型概化存在不确定条件下,边界条件、初始条件、源汇项概化不准确时新方法的有效性。结果表明,当预报模型概化不准确时,使用标准EnKF方法进行数据同化,可能会导致滤波发散,造成同化失败。Bias-CEnKF方法不仅保留了较好的同化性能,同时减小了参数、变量、偏差项非线性关系带来的不一致性。针对文章中4种情景,Bias-CEnKF同化获得的含水层渗透系数场以及水头场均接近真实场,且预报结果可靠。本研究进一步提升了模型概化不确定时EnKF方法的适用性,为实际野外复杂条件下地下水水流数据同化问题提供了可靠的方法。 相似文献
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当精密单点定位的观测值含有异常数据时,Kalman滤波的精度将会降低。采用抗差Kalman滤波方法能够有效抑制观测异常,提高滤波的精度和可靠性。运用武汉国际GPS服务跟踪站数据对该方法进行了验证。结果表明,抗差Kalman滤波的精度比Kalman滤波的精度有一定程度提高,说明抗差Kalman滤波能够有效抑制观测异常。 相似文献
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通过数据同化方法合理地将实时水文观测数据融入到洪水预报模型中,可提高洪水预报模型的实时性和精确度。选取沿程断面流量、水位和糙率系数作为代表水流状态的基本粒子,以监测断面实测水位数据作为观测信息,建立了基于粒子滤波数据同化算法的河道洪水实时概率预报模型。模型应用于黄河中下游河道洪水预报计算的结果表明,采用粒子滤波方法同化观测水位后,不仅可以直接校正水位,同时也可以有效地校正流量和糙率,为未来时刻模型预报计算提供更准确的水流初始条件和糙率取值区间,进而有效地提高模型预报结果的精度,给出合理的概率预报区间。不同预报期的预报结果表明,随着预报期的增长,同化效果减弱,模型预报结果的精度会有所降低,水位概率预报结果受粒子间糙率不同的影响不确定性增加,而流量概率预报结果受给定模型边界条件的影响不确定性降低。所提出模型可以有效同化真实水位观测数据,适合应用于实际的洪水预报工作中。 相似文献
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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
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Reza Tavakoli Gergina Pencheva Mary F. Wheeler Benjamin Ganis 《Computational Geosciences》2013,17(1):83-97
We present a parallel framework for history matching and uncertainty characterization based on the Kalman filter update equation for the application of reservoir simulation. The main advantages of ensemble-based data assimilation methods are that they can handle large-scale numerical models with a high degree of nonlinearity and large amount of data, making them perfectly suited for coupling with a reservoir simulator. However, the sequential implementation is computationally expensive as the methods require relatively high number of reservoir simulation runs. Therefore, the main focus of this work is to develop a parallel data assimilation framework with minimum changes into the reservoir simulator source code. In this framework, multiple concurrent realizations are computed on several partitions of a parallel machine. These realizations are further subdivided among different processors, and communication is performed at data assimilation times. Although this parallel framework is general and can be used for different ensemble techniques, we discuss the methodology and compare results of two algorithms, the ensemble Kalman filter (EnKF) and the ensemble smoother (ES). Computational results show that the absolute runtime is greatly reduced using a parallel implementation versus a serial one. In particular, a parallel efficiency of about 35 % is obtained for the EnKF, and an efficiency of more than 50 % is obtained for the ES. 相似文献
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Data assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data assimilation methods such as ensemble Kalman filter and ensemble smoother approaches, are based on Gaussian assumptions that are not applicable to multimodal random variables. The selection ensemble smoother is introduced as an alternative to traditional ensemble methods. In the proposed method, the prior distribution of the model variables, for example the porosity field, is a selection-Gaussian distribution, which allows modeling of the multimodal behavior of the posterior ensemble. The proposed approach is applied for validation on a two-dimensional synthetic channelized reservoir. In the application, an unknown reservoir model of porosity and permeability is estimated from the measured data. Seismic and production data are assumed to be repeatedly measured in time and the reservoir model is updated every time new data are assimilated. The example shows that the selection ensemble Kalman model improves the characterisation of the bimodality of the model parameters compared to the results of the ensemble smoother.
相似文献14.
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
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Edison Caballero Fernando A. Rochinha Marcio Borges Marcio A. Murad 《国际地质力学数值与分析法杂志》2019,43(2):482-500
In this work, we construct a new methodology for enhancing the predictive accuracy of sequential methods for coupling flow and geomechanics while preserving low computational cost. The new computational approach is developed within the framework of the fixed-stress split algorithm procedure in conjunction with data assimilation based on the ensemble Kalman filter (EnKF). In this context, we identify the high-fidelity model with the two-way formulation where additional source term appears in the flow equation containing the time derivative of total mean stress. The iterative scheme is then interlaced with data assimilation steps, which also incorporate the modeling error inherent to the EnKF framework. Such a procedure gives rise to an “enhanced one-way formulation,” exhibiting substantial improvement in accuracy compared with the classical one-way method. The governing equations are discretized by mixed finite elements, and numerical simulation of a 2D slab problem between injection and production wells illustrate the tremendous achievement of the method proposed herein. 相似文献
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The ensemble Kalman filter has been successfully applied for data assimilation in very large models, including those in reservoir
simulation and weather. Two problems become critical in a standard implementation of the ensemble Kalman filter, however,
when the ensemble size is small. The first is that the ensemble approximation to cross-covariances of model and state variables
to data can indicate the presence of correlations that are not real. These spurious correlations give rise to model or state
variable updates in regions that should not be updated. The second problem is that the number of degrees of freedom in the
ensemble is only as large as the size of the ensemble, so the assimilation of large amounts of precise, independent data is
impossible. Localization of the Kalman gain is almost universal in the weather community, but applications of localization
for the ensemble Kalman filter in porous media flow have been somewhat rare. It has been shown, however, that localization
of updates to regions of non-zero sensitivity or regions of non-zero cross-covariance improves the performance of the EnKF
when the ensemble size is small. Localization is necessary for assimilation of large amounts of independent data. The problem
is to define appropriate localization functions for different types of data and different types of variables. We show that
the knowledge of sensitivity alone is not sufficient for determination of the region of localization. The region depends also
on the prior covariance for model variables and on the past history of data assimilation. Although the goal is to choose localization
functions that are large enough to include the true region of non-zero cross-covariance, for EnKF applications, the choice
of localization function needs to balance the harm done by spurious covariance resulting from small ensembles and the harm
done by excluding real correlations. In this paper, we focus on the distance-based localization and provide insights for choosing
suitable localization functions for data assimilation in multiphase flow problems. In practice, we conclude that it is reasonable
to choose localization functions based on well patterns, that localization function should be larger than regions of non-zero
sensitivity and should extend beyond a single well pattern. 相似文献
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集合—变分数据同化方法的发展与应用 总被引：3，自引：0，他引：3
近年来,集合—变分数据同化方法已成为大气数据同化领域研究的热点问题.该方法能够综合利用集合卡尔曼滤波和变分同化的优势,是实现“集合预报和数据同化一体化”的有效途径.在分析变分同化和集合卡尔曼滤波优缺点的基础上引出集合—变分数据同化的概念;按照不同实现方式,将集合—变分同化分为协方差线性组合和增加控制变量2类,介绍了相应的研究进展,并将集合—变分同化概念拓展;然后介绍了集合—变分同化在英美两国的应用;最后回顾了集合—变分同化研究的主要问题,展望了未来的发展趋势. 相似文献