共查询到18条相似文献，搜索用时 171 毫秒
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岩土力学参数空间变异性的集合卡尔曼滤波估值 总被引：3，自引：1，他引：2
岩土参数具有结构性和随机性的空间变异特征,该特征导致岩土参数具有不确定性。以地质统计学作为岩土参数空间变异性分析的理论基础,将分布于研究区的岩土参数视为区域化变量,变异函数既描述了岩土参数整体的空间结构性变化,又描述了其局部的随机性变化,用变异函数理论模型作为描述岩土参数空间变异规律的数学模型。引入集合卡尔曼滤波（EnKF）分析方法,利用时空分布的观测数据,对岩土参数空间变异性进行估值。数值算例表明,EnKF能够有效地融合观测数据,较好地提供岩土参数空间变异性的估值。 相似文献
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岩土参数的空间变异性分析 总被引：4，自引：0，他引：4
岩土参数具有空间变异性和不确定性,本文将岩土参数视为具有随机性和结构性的区域化变量,利用区域化变量理论和地质统计方法分析上海地基土有代表性的抗剪强度指标的空间变异特性。结果表明该方法是可行的。 相似文献
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借助变异函数的优点，即能够反映区域化变量张开度的空间变化相关性和随机性特征,利用Kriging方法对单裂隙中张开度进行估值，由交叉验证法的拟合结果认为估值结果较为合理，并且通过溶质运移试验验证了Kriging法对单裂隙张开度的估值是可行的。 相似文献
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《岩土力学》2019,(12)
斜坡岩土体抗剪强度参数的空间变异性具有一定的结构性。为研究岩土体参数空间变异结构对边坡失效概率的影响,依据变异函数的内涵推导出变程与相关距离的数学变换关系,并在此基础上提出了结构化交叉约束随机场模拟方法,用以模拟具有互相关性的参数随机场。建立了结构化交叉约束随机场计算模型,研究不同空间变异结构的抗剪强度参数对边坡失效概率的影响。研究结果表明:结构化交叉约束随机场可用于生成模拟具有复杂各向异性空间变异结构的参数随机场,由于考虑了随机偏差、条件数据和空间变异结构,能较为真实地反映地层实际参数,数据波动较条件参数插值场小。可靠性分析结果表明:不考虑抗剪强度参数空间结构分析易高估边坡的失效概率;考虑c′和φ′互相关性时,失效概率随着相关系数的增加而增加,当参数间呈负相关性时更容易高估边坡的失效概率。 相似文献
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《岩土力学》2017,(11):3355-3362
岩土工程可靠度分析和设计中,合理地选取随机场参数和相关函数,并准确地描述土性参数空间变异性十分困难。基于贝叶斯理论,本文提出了一套量化砂土有效内摩擦角空间变异性的方法。该方法根据先验信息和静力触探试验锥尖阻力数据,确定砂土有效内摩擦角的随机场参数和相关函数。该方法合理地考虑了砂土有效内摩擦角与锥尖阻力间经验回归方程的不确定性。采用马尔科夫链蒙特卡洛模拟(Markov Chain Monte Carlo Simulation,MCMCS)获取服从后验分布的随机场参数样本。利用MCMCS样本构建随机场参数的Gaussian Copula函数求解后验分布。估计备选相关函数的概率,选择概率最大的为最可能的相关函数。最后,采用美国德州农工大学国家岩土工程砂土试验场的CPT数据算例验证了文中所提方法的有效性。结果表明:文中所提方法可以正确、合理地利用间接测量的锥尖阻力数据确定砂土有效内摩擦角的随机场参数和相关函数,准确量化其空间变异性。对于美国德州农工大学国家岩土工程砂土试验场的砂土有效内摩擦角,建议选用二阶自回归函数作为其最可能的相关函数。 相似文献
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重质非水相有机污染物(DNAPL)泄漏到地下后,其运移与分布特征受渗透率非均质性影响显著。为刻画DNAPL污染源区结构特征,需进行参数估计以描述水文地质参数的非均质性。本研究构建了基于集合卡尔曼滤波方法(EnKF)与多相流运移模型的同化方案,通过融合DNAPL饱和度观测数据推估非均质介质渗透率空间分布。通过二维砂箱实际与理想算例,验证了同化方法的推估效果,并探讨了不同因素对同化的影响。研究结果表明:基于EnKF方法同化饱和度观测资料可有效地推估非均质渗透率场;参数推估精度随观测时空密度的增大而提高;观测点位置分布对同化效果有所影响,布置在污染集中区域的观测数据对于参数估计具有较高的数据价值。 相似文献
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Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models 总被引：11，自引：0，他引：11
Lin Y. Hu 《Mathematical Geology》2000,32(1):87-108
This paper describes a new method for gradually deforming realizations of Gaussian-related stochastic models while preserving their spatial variability. This method consists in building a stochastic process whose state space is the ensemble of the realizations of a spatial stochastic model. In particular, a stochastic process, built by combining independent Gaussian random functions, is proposed to perform the gradual deformation of realizations. Then, the gradual deformation algorithm is coupled with an optimization algorithm to calibrate realizations of stochastic models to nonlinear data. The method is applied to calibrate a continuous and a discrete synthetic permeability fields to well-test pressure data. The examples illustrate the efficiency of the proposed method. Furthermore, we present some extensions of this method (multidimensional gradual deformation, gradual deformation with respect to structural parameters, and local gradual deformation) that are useful in practice. Although the method described in this paper is operational only in the Gaussian framework (e.g., lognormal model, truncated Gaussian model, etc.), the idea of gradually deforming realizations through a stochastic process remains general and therefore promising even for calibrating non-Gaussian models. 相似文献
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Leslie Smith 《Mathematical Geology》1981,13(1):1-21
The spatial variability in porosity, hydraulic conductivity, compressibility, and various grain size fractions is analyzed for several sets of samples from the Quadra Sand. This unit is a well-sorted, medium grained, horizontally stratified sand with relatively few silt or gravel interbeds. Both random and uniformly spaced sample plans are used. The heterogeneity of the flow parameters is characterized by frequency histograms and their estimated moments, by their sample autocorrelation functions, and the estimated power spectra. Emphasis is placed on the nature of the spatial dependence between neighboring values of the flow parameters. A nearest neighbor stochastic process model is fit to the data to consider its adequacy in describing the spatial dependence within the porosity and hydraulic conductivity sequences. Even though the Quadra Sand is relatively uniform, a fairly complex spatial structure is observed. A simple monotonically decaying autocorrelation function may not adequately represent the spatial continuity. Statistical anisotropy is observed in both the extent of the spatial autocorrelation and in its functional form. Results show the importance of scale in constructing a probability model to describe the spatial variability. 相似文献
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G. Y. Sheu 《Geotechnical and Geological Engineering》2013,31(5):1453-1464
Originating an attempt of understanding the reliability and serviceability of foundations, an interest of comparing the difference between settlements predicted with and without considering the uncertainty in such as the spatial variability of soil properties is born. This study selectively compares between settlements predicted with and without considering the uncertainty in the spatial variability of Young’s modulus. The tool is a coupling of perturbation expansions of Young moduli and a two-dimensional meshfree weak-strong form in elastostatics. Two further examples show that the spatial variability of Young’s modulus causes apparent difference between probabilistic and deterministic settlement components along the direction of a surcharge. We can derive an autocorrelation function to describe the spatial variability of Young’s modulus and understand how it affects predicted settlements depending upon autocorrelation function values. In addition, the spectral stochastic meshless local Petrov–Galerkin method is a time-saving tool for predicting probabilistic settlements with the uncertainty in the spatial variability of soil properties. 相似文献
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地质统计学是用于模拟土体固有空间变异性的方法之一,以变差函数为工具,采用Kriging插值提供未采样点处土工参数值的最优线性无偏估计。将地质统计学方法应用于宿-新（宿迁至新沂）高速公路某试验段内孔压静力触探（piezocone penetration test,CPTU）锥尖阻力qt空间变异性研究中,采用回归分析移除数据中的趋势项,从而获得具有弱平稳性的残差数据。指数型理论变差函数能够准确描述试验段内土体的连续空间变异性特征。根据估计结果,试验段内锥尖阻力qt残差的变程具有显著各向异性,在水平方向和竖直方向分别为4.05 m和1.2 m。采用普通Kriging插值结合趋势分析,绘制了qt在试验段的空间分布图和平面投影图,用于指导工程实践。结果表明,普通Kriging插值的估计结果能够与试验段内实测资料形成较好的对比,仅仅在部分极值变化和远离采样点的位置处估计值可靠性会降低。 相似文献
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渗透性参数非均质特征的研究进展 总被引：6，自引：1，他引：6
尺度效应、非一致性、随机性和结构性是渗透性参数的四大特征，通过分析比较独立离散参数法、连续相关参数法、离散相关参数法、条件模拟，认为地质统计学的理论与方法是非均质问题研究的理想工具，并提出了今后研究的趋势。 相似文献
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Humberto Yáñez-Godoy Abdelhammid Mokeddem Sidi Mohammed Elachachi 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(4):299-314
The uncertainty in terms of soil characterisation is studied to assess its effect on the structural behaviour of extended structures as sheet pile walls. A finite element model is used. This integrates a numerical model of the soil–structure interaction together with a stochastic model that allows characterising the soil variability. The model serves in propagating the variability and the system parameter uncertainties. Discussion is mainly focused on two points: (1) testing the sensitivity of the structural behaviour of a sheet pile wall to different geotechnical parameters and (2) assessing the influence of spatial variability of soil properties on the structural behaviour by identifying the most sensitive geotechnical parameter and the most significant correlation length values. The findings showed that in assessing the sheet pile wall’s structural behaviour, there are spatial variability parameters that cannot be considered negligible. In this study, soil friction angle is found to be an important parameter. 相似文献
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Based on the algorithm for gradual deformation of Gaussian stochastic models, we propose, in this paper, an extension of this method to gradually deforming realizations generated by sequential, not necessarily Gaussian, simulation. As in the Gaussian case, gradual deformation of a sequential simulation preserves spatial variability of the stochastic model and yields in general a regular objective function that can be minimized by an efficient optimization algorithm (e.g., a gradient-based algorithm). Furthermore, we discuss the local gradual deformation and the gradual deformation with respect to the structural parameters (mean, variance, and variogram range, etc.) of realizations generated by sequential simulation. Local gradual deformation may significantly improve calibration speed in the case where observations are scattered in different zones of a field. Gradual deformation with respect to structural parameters is necessary when these parameters cannot be inferred a priori and need to be determined using an inverse procedure. A synthetic example inspired from a real oil field is presented to illustrate different aspects of this approach. Results from this case study demonstrate the efficiency of the gradual deformation approach for constraining facies models generated by sequential indicator simulation. They also show the potential applicability of the proposed approach to complex real cases. 相似文献
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Constraining stochastic models of reservoir properties such as porosity and permeability can be formulated as an optimization problem. While an optimization based on random search methods preserves the spatial variability of the stochastic model, it is prohibitively computer intensive. In contrast, gradient search methods may be very efficient but it does not preserve the spatial variability of the stochastic model. The gradual deformation method allows for modifying a reservoir model (i.e., realization of the stochastic model) from a small number of parameters while preserving its spatial variability. It can be considered as a first step towards the merger of random and gradient search methods. The gradual deformation method yields chains of reservoir models that can be investigated successively to identify an optimal reservoir model. The investigation of each chain is based on gradient computations, but the building of chains of reservoir models is random. In this paper, we propose an algorithm that further improves the efficiency of the gradual deformation method. Contrary to the previous gradual deformation method, we also use gradient information to build chains of reservoir models. The idea is to combine the initial reservoir model or the previously optimized reservoir model with a compound reservoir model. This compound model is a linear combination of a set of independent reservoir models. The combination coefficients are calculated so that the search direction from the initial model is as close as possible to the gradient search direction. This new gradual deformation scheme allows us for reducing the number of optimization parameters while selecting an optimal search direction. The numerical example compares the performance of the new gradual deformation scheme with that of the traditional one. 相似文献