多孔介质随机参数的相关性对非饱和水流的影响

基于van Genuchten-Mualem非饱和水分特征模型,建立了非饱和流运动的随机数值模型。将饱和水力传导度和孔隙大小分布参数视为服从对数正态分布的随机场,用Karhunen-Loeve展开分解,水头表示为混沌多项式展开。通过摄动方法得到一系列关于水头展开的偏微分方程,并用有限差分法进行求解。应用本文的模型分析了两随机场在统计不相关和完全相关模式下对水流随机分析的影响,结果表明两种模式下的水头均值相同,完全相关模式下的水头标准差较不相关模式下的明显偏小。     

考虑参数旋转各向异性空间变异性的边坡大变形概率分析

为揭示土体参数旋转各向异性空间变异性对边坡大变形特征的影响规律,将土体不排水抗剪强度参数模拟为旋转各向异性随机场,提出采用多重响应面法对随机场样本进行边坡稳定性安全系数高效求解和升序排列,进而使用随机物质点法按序模拟随机场样本的边坡大变形过程.以一饱和不排水粘土边坡为例,研究了旋转各向异性随机场的旋转角度β和弱主方向自相关长度θ₂对边坡大变形特征和破坏模式的影响.结果表明：提出的基于多重响应面的随机物质点方法可以高效开展边坡大变形概率分析;β和θ₂对边坡大变形特征和破坏模式均有显著影响;边坡大变形破坏的影响距离、滑动距离和滑动体积的平均值与标准差均随θ₂的增加而增加;边坡大变形过程中可能产生4种破坏模式,其中深层滑动和渐进式滑动为该边坡模型的主要概率失稳模式.因此,提出的方法为边坡大变形概率分析提供了一条有效途径,考虑参数旋转各向异性空间变异性的边坡大变形概率分析对准确评估边坡失稳风险具有一定的理论参考意义.     

考虑参数空间变异结构的结构化交叉约束随机场模拟方法研究

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。     

渗透率空间变异性对重非水相流体运移的影响

中针对多孔介质中水、气和非水相流体的多相流运移特点,建立了由包气带至饱和带的 NAPLs- 水 - 气三相流模 型。用 Karhunen-Loeve 展开方法构建 4 个空间变异性不同的渗透率随机场。在此基础上,分别模拟 4 种情景重非水相流体 (DNAPLs)的泄漏运移过程。通过分析 DNAPLs 饱和度的空间分布以及其空间矩随时间的变化来探讨渗透率空间变异性对 DNAPLs 运移的影响。结果显示,随着渗透率空间变异性的增强,DNAPLs 饱和度的空间变异性增大。渗透率场中的优势通 道对 DNAPLs 运移有着显著影响,直接决定 DNAPLs 的运移路径和饱和度分布。渗透率场的各向异性降低了 DNAPLs 在垂向 上的运移速度,影响污染羽的形态。     

不排水抗剪强度非平稳随机场模拟及边坡可靠度分析

边坡可靠度分析中通常假定采用平稳或准平稳随机场表征土体参数的空间变异性,然而大量现场试验数据表明,土体参数如不排水抗剪强度沿土体埋深常呈现明显的非平稳分布特征,即其均值和标准差均随埋深发生变化,因此亟需发展土体参数非平稳随机场模型及其模拟方法。针对目前不能有效单独模拟土体参数趋势分量和随机波动分量的不确定性,提出了一种有效的不排水抗剪强度参数非平稳随机场模型,并给出了土体参数二维非平稳随机场模拟方法计算流程,同时将新提出的模型与现有非平稳随机场模型及平稳随机场模型进行了系统比较。最后通过不排水饱和黏土边坡算例验证了提出模型的有效性,并揭示了不排水抗剪强度非平稳分布特征对边坡可靠度的影响规律。结果表明：提出模型能够有效地单独模拟土体参数趋势分量和随机波动分量的不确定性,考虑土体参数均值和标准差随埋深增加而增大的特性,可为表征土体参数非平稳分布特征提供了一条有效的途径。此外,与采用非平稳随机场模拟土体参数空间变异性相比,采用常用的平稳随机场模型会低估边坡失效概率,从而造成偏危险的边坡工程设计方案。     

基于饱和渗透系数空间变异结构的斜坡渗流及失稳特征

以往研究一般采用单随机变量方法（SRV）或基于水平或垂直方向波动范围生成的空间变异随机场来模拟岩土参数的空间变异性,对具有倾斜定向特征的空间变异随机场未有涉及.基于条件模拟相关理论和非侵入式随机有限元的理论框架,提出了利用序贯高斯模拟方法进行斜坡参数条件随机场模拟并运用有限元方法进行斜坡渗流和稳定性分析的方法.针对理想边坡,对各向同性和几何各向异性的共7种空间变异结构的饱和渗透系数（K_s）各进行了200次条件随机场模拟,基于条件随机场模拟结果进行了有限元渗流和稳定性计算,对每种空间变异结构多次计算结果进行了统计分析.结果表明:本文所提出的方法不仅再现了研究区域参数的空间二阶统计特性,通过设定变异函数参数进行不同空间变异类型、变异程度、变异定向性的随机场模拟,同时利用现场观测数据对随机场模拟结果进行条件限制,从而提高了随机场的赋值精度;K_s的空间变异结构对孔隙水压力的分布规律、地下水位线变化范围、稳定性系数和最危险滑动面分布特征均有一定程度的影响.本研究为库岸斜坡稳定性评价提供方法支撑.      

Bayes约束随机场下坝基溶蚀区随机模拟方法及其影响分析

基于完全随机场模拟溶蚀岩体可能会高估其空间变异性和不能有效地利用溶蚀以外的地质信息和实践经验,提出用Bayes约束参数随机场模型描述坝基溶蚀区的随机模拟方法。引入Bayes公式,对溶蚀区域岩土参数的统计特性进行修正,反映出地质勘测的新增地质信息和试验参数信息,建立约束随机场,并在此基础上进行随机有限元分析,研究坝基溶蚀对大坝结构性态的作用效应。计算结果表明,相对于完全随机场模型,Bayes约束随机场模型更为客观地考虑了溶蚀岩体的空间变异性,有效地降低了溶蚀参数完全随机场下结构响应的模拟方差。在概率分析过程中,推荐蒙特卡洛响应面耦合方法（MC-RSM）作为适用于复杂工程的随机模拟方法,该方法能够代替直接MC法对同样力学机制的不断重复,减小计算时间成本。     

岩土参数随机场离散的三角形单元局部平均法

将不确定性岩土参数建模为随机场而非传统意义上的随机变量,基于随机场的局部平均理论,提出了用于二维随机场离散的三角形单元局部平均法。通过面积坐标变换和高斯数值积分,给出了三角形单元局部平均随机场协方差矩阵的解析计算方法和数值计算方法。采用算例再现了所提方法的分析过程和有效性,并与传统二维随机场四边形单元离散法进行了对比。结果表明:提出的二维随机场三角形单元离散法能与有限元三角形单元离散法完美结合,随机场单元与有限元单元的对应关系清晰,易于随机有限元程序的编制;对于随机场单元的均值,传统四边形单元离散法与所提方法的计算结果相同;对于随机场单元的方差,传统四边形单元离散法计算结果偏小,所提方法显得更加科学、合理。     

2.5维各向异性介质中地震波场数值模拟

这里实现了在2.5维各向异性介质中地震波传播的数值模拟。首先从2.5维一阶速度-应力弹性各向异性波动方程出发,得出了该方程的拟谱法数值解法;然后通过对Marmousi速度模型进行数值模拟,表明了拟谱法的有效性;最后计算并分析了均匀和混合各向异性介质中波的传播快照和理论模拟的地震记录,进一步认识了波在各向异性介质中的传播规律。     

浅基础沉降可靠性的Neumann随机有限元分析

将随机土性分布参数模拟为随机场，用矩形和任意四边形局部平均随机场单元来离散随机场，对随机场离散技术和自协方差矩阵的组集方法进行了研究，提出了多维相关正态分布随机数的计算机模拟方法，同摄动随机有限元和直接Monte-Carlo随机有限元相比，Neumann随机有限元具有较好的计算精度和计算效率，研制了Neumann随机有限元的程度，考虑荷载变异性和土性分布参数的变异性，用Neumann随机有限元法进行地基沉降和非均沉降的概率分析。     

KL变换与小波变换联合去噪新方法

常规KL变换对于具有强随机噪声的地震信号处理效果不佳,本文提出一种将小波变换和KL变换结合使用的去噪处理新方法。经合成剖面和实际剖面证明,该方法能有效去除强随机噪声,提高地震剖面信噪比      

A stochastic collocation based Kalman filter for data assimilation

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.     

History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique

Traditional ensemble-based history matching method, such as the ensemble Kalman filter and iterative ensemble filters, usually update reservoir parameter fields using numerical grid-based parameterization. Although a parameter constraint term in the objective function for deriving these methods exists, it is difficult to preserve the geological continuity of the parameter field in the updating process of these methods; this is especially the case in the estimation of statistically anisotropic fields (such as a statistically anisotropic Gaussian field and facies field with elongated facies) with uncertainties about the anisotropy direction. In this work, we propose a Karhunen-Loeve expansion-based global parameterization technique that is combined with the ensemble-based history matching method for inverse modeling of statistically anisotropic fields. By using the Karhunen-Loeve expansion, a Gaussian random field can be parameterized by a group of independent Gaussian random variables. For a facies field, we combine the Karhunen-Loeve expansion and the level set technique to perform the parameterization; that is, for each facies, we use a Gaussian random field and a level set algorithm to parameterize it, and the Gaussian random field is further parameterized by the Karhunen-Loeve expansion. We treat the independent Gaussian random variables in the Karhunen-Loeve expansion as the model parameters. When the anisotropy direction of the statistically anisotropic field is uncertain, we also treat it as a model parameter for updating. After model parameterization, we use the ensemble randomized maximum likelihood filter to perform history matching. Because of the nature of the Karhunen-Loeve expansion, the geostatistical characteristics of the parameter field can be preserved in the updating process. Synthetic cases are set up to test the performance of the proposed method. Numerical results show that the proposed method is suitable for estimating statistically anisotropic fields.     

基于小波包分析的地震资料联合去噪方法研究

在地震记录中,随机噪声严重影响了有效信号的提取,为此必须进行消噪处理。这里首先使用小波包变换对不同频段的信号进行精细分离,有效信号和噪声经小波包分解后,其小波包系数将表现出不同特性,然后根据这种不同特性进行去噪处理,对小波包分析法处理后的剩余地震信号再进行KL(Karhunen-Loeve)变换,提取相关有效信号,最后对提取的有效信号进行中值滤波处理,进一步去除剩余噪声。经合成地震剖面和实际地震剖面处理实验证明,小波包分析、KL变换和中值滤波联合去噪方法,能有效地消除较强的随机噪声,提高地震剖面信噪比和分辨率。     

Spatial variability effect of internal friction angle on the post-failure behavior of landslides using a random and non-Newtonian fluid based SPH method

This study proposed a random Smoothed Particle Hydrodynamics method for analyzing the post-failure behavior of landslides,which is based on the Karhunen-Loeve(K-L) expansion,the non-Newtonian fluid model,and the OpenMP parallel framework.Then,the applicability of this method was validated by comparing the generated random field with theoretical result and by simulating the post-failure process of an actual landslide.Thereafter,an illustrative landslide example was created and simulated to obtain the spatial variability effect of internal friction angle on the post-failure behavior of landslides under different coefficients of variation(COVs) and correlation lengths(CLs).As a conclusion,the reinforcement with materials of a larger friction angle can reduce the runout distance and impact the force of a landslide.As the increase of COV,the distribution range of influence zones also increases,which indicates that the deviation of influence zones becomes large.In addition,the correlation length in Monte Carlo simulations should not be too small,otherwise the variation range of influence zones will be underestimated.     

应用改进的KL变换提高叠后地震记录信噪比

常规KL变换叠后去噪仅能加强水平方向同相轴,对倾斜或弯曲同相轴处理效果较差,对能量较强的相干噪声去除效果不佳,且在处理大量数据时计算成本很高,在实际生产中难以广泛应用。本文针对常规KL变换的缺陷分别进行了三点改进:使用倾角扫描叠加KL变换,可在有效压制地震剖面噪声的同时,较好保持倾斜同相轴和弯曲同相轴;使用本文提出的时空变-倾角KL变换能去除能量较强的相干噪声;使用数据分块技术可减少运算量。模型测试和实际资料处理效果表明:改进后的KL变换适应性强,去噪效果大大改善。      

Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics

This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion, also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves only the two-point statistics of a random field, which is insufficient for reproducing complex structures. In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely used in machine learning applications, it does not appear to have found any application for geological model parameterization. With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels, which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can be used with gradient-based history matching to provide models that match production history while maintaining multipoint geostatistics consistent with the underlying training image.     

应用时空变—倾角扫描叠加KL变换提高地震资料信噪比

使用KL变换可去除地震资料中随机噪声和相干噪声，提高地震资料信噪比。但常规KL变换仅能加强水平方向同相轴，对倾斜或弯曲同相轴处理效果较差，且在处理大量数据时计算成本很高，在实际生产中难以广泛应用。一些文献中针对常规KL变换的两个缺陷进行了改进，即使用倾角扫描叠加KL变换和采用数据分块技术。作者在本文中将这两个方面的改进有机地结合起来，提出应用时空变－倾角扫描叠加KL变换对地震资料进行处理，并指出使用该方法时需要注意的一些问题。理论模型和实际资料处理效果表明，使用本方法可以克服常规KL变换的缺陷，有效改进剖面处理效果。     

用Monte Carlo方法模拟砂土的自然堆积过程

用MonteCarlo随机模拟方法模拟砂土颗粒在重力作用下,在圆柱与长方体容器中的自然堆积过程。首先用参考网格法生成一个砂土颗粒的松散结构,松散结构中粒子与粒子、粒子与边界间不存在任何接触;然后启动MonteCarlo随机模拟算法,即给处于松散结构中的每个粒子施加随机位移,得到新构形,如果新构形中粒子间或粒子与边界间发生重叠,则放弃这个构形;如果没有重叠存在,则判别粒子体系的势能变化,运用Metropolis准则来判别这个构形是否被接受,重复这个过程可以得到砂土的密集堆积结构。采用Schinner建议的接触发现算法判别粒子间是否存在重叠,同时详细介绍了粒子与边界间的接触发现算法。模拟结果表明,用MonteCarlo方法模拟砂土的自然堆积结构是非常有效的,可以为砂土的流动、压实等的数值模拟工作提供初始构形。     

含缺陷空间变异性岩体裂纹扩展的近场动力学模拟

天然岩体内部含有微裂纹,其裂纹的发展贯通机制一直是岩石力学研究的热点问题。此外,岩体物理力学参数的空间变异性,也会影响其强度及破坏演化过程。本文采用了一种新的随机近场动力学方法(RPD),并对含缺陷岩体单轴压缩下的裂纹发展进行模拟,该方法结合了近场动力学方法在模拟裂纹发展及随机场方法在表征材料参数空间变异性方面的优势,使所建模型更接近真实岩体状态。利用MATLAB编程并依托含单一倾斜预制裂缝模型进行了计算程序的准确性验证。结果表明,所采用的随机近场动力学方法可以很好地模拟裂纹发展过程,预制裂缝倾角对后续裂纹发展有较大影响。此外,不考虑岩体物理力学材料参数空间变异性的存在时,会大大低估裂纹发展速度。