Conditional random field reliability analysis of a cohesion-frictional slope

Discarding known data from cored samples in the reliability analysis of a slope in spatially variable soils is a waste of site investigation effort. The traditional unconditional random field simulation, which neglects these known data, may overestimate the simulation variance of the underlying random fields of the soil properties. This paper attempts to evaluate the reliability of a slope in spatially variable soils while considering the known data at particular locations. Conditional random fields are simulated based on the Kriging method and the Cholesky decomposition technique to match the known data at measured locations. Subset simulation (SS) is then performed to calculate the probability of slope failure. A hypothetical homogeneous cohesion-frictional slope is taken as an example to investigate its reliability conditioned on several virtual samples. Various parametric studies are performed to explore the effect of different layouts of the virtual samples on the factor of safety (FS), the spatial variation of the critical slip surface and the probability of slope failure. The results suggest that whether the conditional random fields can be accurately simulated depends highly on the ratio of the sample distance and the autocorrelation distance. Better simulation results are obtained with smaller ratios. Additionally, compared with unconditional random field simulations, conditional random field simulations can significantly reduce the simulation variance, which leads to a narrower variation range of the FS and its location and a much lower probability of failure. The results also highlight the great significance of the conditional random field simulation at relatively large autocorrelation distances.     

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.      

Conditional simulation of intrinsic random functions of order k

Conditional simulation of intrinsic random functions of orderk is a stochastic method that generates realizations which mimic the spatial fluctuation of nonstationary phenomena, reproduce their generalized covariance and honor the available data at sampled locations. The technique proposed here requires the following steps: (i) on-line simulation of Wiener-Levy processes and of their integrations; (ii) use of the turning-bands method to generate realizations in Rⁿ; (iii) conditioning to available data; and (iv) verification of the reproduced generalized covariance using generalized variograms. The applicational aspects of the technique are demonstrated in two and three dimensions. Examples include the conditional simulation of geological variates of the Crystal Viking petroleum reservoir, Alberta, Canada.     

Conditional simulation of intrinsic random functions of orderk

Conditional simulation of intrinsic random functions of orderk is a stochastic method that generates realizations which mimic the spatial fluctuation of nonstationary phenomena, reproduce their generalized covariance and honor the available data at sampled locations. The technique proposed here requires the following steps: (i) on-line simulation of Wiener-Levy processes and of their integrations; (ii) use of the turning-bands method to generate realizations in Rⁿ; (iii) conditioning to available data; and (iv) verification of the reproduced generalized covariance using generalized variograms. The applicational aspects of the technique are demonstrated in two and three dimensions. Examples include the conditional simulation of geological variates of the Crystal Viking petroleum reservoir, Alberta, Canada.     

Uncertainty reduction and sampling efficiency in slope designs using 3D conditional random fields

A method of combining 3D Kriging for geotechnical sampling schemes with an existing random field generator is presented and validated. Conditional random fields of soil heterogeneity are then linked with finite elements, within a Monte Carlo framework, to investigate optimum sampling locations and the cost-effective design of a slope. The results clearly demonstrate the potential of 3D conditional simulation in directing exploration programmes and designing cost-saving structures; that is, by reducing uncertainty and improving the confidence in a project’s success. Moreover, for the problems analysed, an optimal sampling distance of half the horizontal scale of fluctuation was identified.     

A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF)

In the past years, many applications of history-matching methods in general and ensemble Kalman filter in particular have been proposed, especially in order to estimate fields that provide uncertainty in the stochastic process defined by the dynamical system of hydrocarbon recovery. Such fields can be permeability fields or porosity fields, but can also fields defined by the rock type (facies fields). The estimation of the boundaries of the geologic facies with ensemble Kalman filter (EnKF) was made, in different papers, with the aid of Gaussian random fields, which were truncated using various schemes and introduced in a history-matching process. In this paper, we estimate, in the frame of the EnKF process, the locations of three facies types that occur into a reservoir domain, with the property that each two could have a contact. The geological simulation model is a form of the general truncated plurigaussian method. The difference with other approaches consists in how the truncation scheme is introduced and in the observation operator of the facies types at the well locations. The projection from the continuous space of the Gaussian fields into the discrete space of the facies fields is realized through in an intermediary space (space with probabilities). This space connects the observation operator of the facies types at the well locations with the geological simulation model. We will test the model using a 2D reservoir which is connected with the EnKF method as a data assimilation technique. We will use different geostatistical properties for the Gaussian fields and different levels of the uncertainty introduced in the model parameters and also in the construction of the Gaussian fields.     

Markov chain Monte Carlo methods for conditioning a permeability field to pressure data

Generating one realization of a random permeability field that is consistent with observed pressure data and a known variogram model is not a difficult problem. If, however, one wants to investigate the uncertainty of reservior behavior, one must generate a large number of realizations and ensure that the distribution of realizations properly reflects the uncertainty in reservoir properties. The most widely used method for conditioning permeability fields to production data has been the method of simulated annealing, in which practitioners attempt to minimize the difference between the ’ ’true and simulated production data, and “true” and simulated variograms. Unfortunately, the meaning of the resulting realization is not clear and the method can be extremely slow. In this paper, we present an alternative approach to generating realizations that are conditional to pressure data, focusing on the distribution of realizations and on the efficiency of the method. Under certain conditions that can be verified easily, the Markov chain Monte Carlo method is known to produce states whose frequencies of appearance correspond to a given probability distribution, so we use this method to generate the realizations. To make the method more efficient, we perturb the states in such a way that the variogram is satisfied automatically and the pressure data are approximately matched at every step. These perturbations make use of sensitivity coefficients calculated from the reservoir simulator.     

Closed Form Solutions of the Two-Dimensional Turning Bands Equation

The turning bands method generates realizations of isotropic Gaussian random fields by means of appropriately summed line processes. For two-dimensional simulations the relation between the isotropic correlation function of the random field and the correlation function to be simulated along the lines is given by an integral equation of Abel type. We present closed form solutions of this integral equation for almost all two-dimensional correlation models encountered in practice and discuss their numerical implementation. As an additional benefit, our tables and illustrations serve as a concise guide to correlation models useful in geostatistics.     

Joint High-Order Simulation of Spatially Correlated Variables Using High-Order Spatial Statistics

Joint geostatistical simulation techniques are used to quantify uncertainty for spatially correlated attributes, including mineral deposits, petroleum reservoirs, hydrogeological horizons, environmental contaminants. Existing joint simulation methods consider only second-order spatial statistics and Gaussian processes. Motivated by the presence of relatively large datasets for multiple correlated variables that typically are available from mineral deposits and the effects of complex spatial connectivity between grades on the subsequent use of simulated realizations, this paper presents a new approach for the joint high-order simulation of spatially correlated random fields. First, a vector random function is orthogonalized with a new decorrelation algorithm into independent factors using the so-termed diagonal domination condition of high-order cumulants. Each of the factors is then simulated independently using a high-order univariate simulation method on the basis of high-order spatial cumulants and Legendre polynomials. Finally, attributes of interest are reconstructed through the back-transformation of the simulated factors. In contrast to state-of-the-art methods, the decorrelation step of the proposed approach not only considers the covariance matrix, but also high-order statistics to obtain independent non-Gaussian factors. The intricacies of the application of the proposed method are shown with a dataset from a multi-element iron ore deposit. The application shows the reproduction of high-order spatial statistics of available data by the jointly simulated attributes.     

三峡水库某库段岩体裂隙网络模拟研究

为合理评价三峡水库某库岸岩体结构特征及工程整治提供依据,对区内岩体进行了裂隙调查及网络模拟研究。研究基于Monte-Carlo模拟方法,在技术上进行了探讨和一定改进。如采用了聚类分析法确定裂隙优势产状、用直接法产生随机数、广义RQD t及裂隙线连通率的计算等。根据野外调查及计算机模拟研究,研究区发育有3组优势裂隙、平均迹长1.45 m左右。间距一般服从对数正态或负指数分布,而走向和迹长基本上不服从常规分布。裂隙线密度在3－6条/m、t = 0.1 m时RQD t值在90 %左右、t = 0.7 m时RQD t值为10 %－20 %、RQD t值变化可能影响库岸岩体在不同方向的稳定性。     

Digital simulation of multivariate two- and three-dimensional stochastic processes with a spectral turning bands method

The space domain version of the turning bands method can simulate multidimensional stochastic processes (random fields) having particular forms of covariance functions. To alleviate this limitation a spectral representation of the turning bands method in the two-dimensional case has shown that the spectral approach allows simulation of isotropic two-dimensional processes having any covariance or spectral density function. The present paper extends the spectral turning bands method (STBM) even further for simulation of much more general classes of multidimensional stochastic processes. Particular extensions include: (i) simulation of three-dimensional processes using STBM, (ii) simulation of anisotropic two- or three-dimensional stochastic processes, (iii) simulation of multivariate stochastic processes, and (iv) simulation of spatial averaged (integrated) processes. The turning bands method transforms the multidimensional simulation problem into a sum of a series of one-dimensional simulations. Explicit and simple expressions relating the cross-spectral density functions of the one-dimensional processes to the cross-spectral density function of the multidimensional process are derived. Using such expressions the one-dimensional processes can be simulated using a simple one-dimensional spectral method. Examples illustrating that the spectral turning bands method preserves the theoretical statistics are presented. The spectral turning bands method is inexpensive in terms of computer time compared to other multidimensional simulation methods. In fact, the cost of the turning bands method grows as the square root or the cubic root of the number of points simulated in the discretized random field, in the two- or three-dimensional case, respectively, whereas the cost of other multidimensional methods grows linearly with the number of simulated points. The spectral turning bands method currently is being used in hydrologic applications. This method is also applicable to other fields where multidimensional simulations are needed, e.g., mining, oil reservoir modeling, geophysics, remote sensing, etc.     

Analysis and Estimation of Natural Processes with Nonhomogeneous Spatial Variation Using Secondary Information

Natural processes encountered in mining, hydrogeologic, environmental, etc. applications usually are poorly known because of scarcity of data over the area of interest. Therefore, stochastic estimation techniques are the tool of choice for a careful accounting of the heterogeneity and uncertainty involved. Within such a framework, a better utilization of all available data concerning the process of interest and all other natural processes related to it, is of primary importance. Because many natural processes show complicated spatial trends, the hypothesis of spatial homogeneity cannot be invoked always, and the more general theory of intrinsic spatial random fields should be employed. Efficient use of secondary information in terms of the intrinsic model requires that suitable permissibility criteria for the generalized covariances and cross-covariances are satisfied. A set of permissibility criteria are presented for the situation of two intrinsic random fields. These criteria are more general and comprehensive than the ones currently available in the geostatistical literature. A constrained least-square technique is implemented for the inference of the generalized covariance and cross-covariance parameters, and a synthetic example is used to illustrate the methodology. The numerical results show that the use of secondary information can lead to significant reductions in the estimation errors.     

边坡稳定可靠度分析的广义可行方向法

在广义随机空间中直接建立验算点的迭代公式,可以方便地解决含相关随机变量的结构可靠度计算问题。在广义随机空间中推导了一种收敛性较好的验算点迭代公式,并应用于边坡稳定的可靠度分析。针对卡基娃混凝土面板堆石坝地震工况坝坡稳定的可靠度分析表明:(1)由于安全系数法采用的输入参数为确定值,故不能考虑参数随机波动的影响。可靠度分析能够反映参数随机性的影响,这对于深入评估工程安全与抗风险能力是有益的。(2)通过引入条件概率方法考虑地震和坝坡失稳同时发生的概率得到的结构可靠指标将提高,而提高的幅度与地震发生概率的大小有关。(3)堆石非线性强度指标具有正相关性,考虑强度指标的相关性将提高边坡稳定的可靠度。     

Multivariate Intrinsic Random Functions for Cokriging

In multivariate geostatistics, suppose that we relax the usual second-order-stationarity assumptions and assume that the component processes are intrinsic random functions of general orders. In this article, we introduce a generalized cross-covariance function to describe the spatial cross-dependencies in multivariate intrinsic random functions. A nonparametric method is then proposed for its estimation. Based on this class of generalized cross-covariance functions, we give cokriging equations for multivariate intrinsic random functions in the presence of measurement error. A simulation is presented that demonstrates the accuracy of the proposed nonparametric estimation method. Finally, an application is given to a dataset of plutonium and americium concentrations collected from a region of the Nevada Test Site used for atomic-bomb testing.     

Incorporation of fracture directions into 3D geostatistical methods for a rock fracture system

Simulating a rock fracture distribution is an important problem common to various fields in geosciences. This paper presents GEOFRAC, a geostatistical method to simulate a fracture distribution by incorporating the directions (strikes and dips) of the sampled fracture data into the simulation. Fracture locations are generated randomly following fracture densities assigned by a sequential Gaussian simulation. Fracture directions are transformed into an indicator set consisting of several binary (0 and 1) variables and the variables are compressed using the principal component analysis. Ordinary kriging is then employed to estimate the distributions of these principal values and the results are back-transformed into the coordinate system of the original indicator set. Fracture directions are generated randomly using their histograms within the defined directional interval. Finally, facets (fracture elements) are determined from the simulated locations and directions, and the fractures within the angle and distance tolerances are connected to form a fracture plane. From a case study of applying GEOFRAC to the fracture data in Kikuma granite, southwest Japan, GEOFRAC was shown to be able to depict a plausible fracture system because the simulated directions corresponded well to those measured. Furthermore, the simulated fracture system was available to estimate the hydraulic conductivity of the study site, which was roughly in agreement with the average of hydraulic test results.     

约束随机场下的边坡可靠度随机有限元分析方法

目前边坡可靠度中常用的简化分析方法,不考虑边坡土体的空间变异性,每次计算整个边坡都取用相同的强度参数,由离散点试样试验得到的土体参数统计特性只能反映点特性,而边坡的稳定性受滑面上平均抗剪强度特性控制,因此,需要考虑空间范围内的平均特性。描述空间变异性的随机场理论对变异性较高的土体,实际上高估了其空间变异性。把随机场理论和地质统计中的区域化变量理论结合起来,建立约束随机场,并在此基础上进行Monte-Carlo随机有限元分析。计算实例表明,在高变异性条件下约束随机场能有效降低完全随机场的模拟方差,得到更低的破坏概率。对比了随机有限元和简化法的计算结果表明,简化法在土体强度变异性很高时其结果并非偏于保守。另外也指出了可靠度分析中存在的边坡尺度效应和简化法的适用条件。     

An anemometry technique for turbulence measurements at low velocities

A method using symmetrically bent V-shaped hot-wires has been developed for the accurate measurement of low-speed turbulence. Directional characteristics of V-shaped hot-wires at low velocities are investigated, and a generalized expression is derived for the effective cooling velocity. The measurement with V-shaped hot-wires in a pseudo turbulent field, which is artificially produced by shaking the hot-wires with an accurately known motion in a steady flow, has confirmed that the expression for the effective cooling velocity is also valid for instantaneous velocity fluctuations. The accuracy of a practical technique comprising two V-shaped hot-wires in an X arrangement is investigated by an error analysis in simulated Gaussian velocity fields using a digital computer.     

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。     

An efficient method of generating random permeability fields by the source point method

Aquifer properties, for example permeability and porosity, vary in space and may be characterized by their distributions. The property distribution is not totally random but shows some correlation structure. Because most of the values are not known, some rational method is required to generate credible aquifer distribution properties for inclusion in fluid transport models. This paper presents a numerically efficient method of generating geostatistical random fields, by the source Point Method (SPM). The SPM is a very efficient method and requires little computer time and relatively small data storage, as compared to other methods of generating random fields. In addition, the SPM is modified to include any desired amount of anisotropy in the property distribution of a system. By using conditional covariances, a formula for a two-dimensional anisotropic field is derived to prespecify the desired correlation length in any direction. Results show that for an anisotropic medium the correlation length can be pre-specified in any specific direction.     

Gaussian Cosimulation: Modelling of the Cross-Covariance

Whenever two or more random fields are assumed to be correlated in reservoir characterization, it is necessary to generate valid cross-covariance models to describe the relationship. The standard methods for constructing covariance matrices for correlated random fields are not very general. In particular, they do not allow one to specify different auto-covariance models for the two fields. It is not possible, for example, for one field to have a Gaussian auto-covariance and the other an exponential auto-covariance, unless the two fields are uncorrelated. The standard approaches also do not allow for nonsymmetric cross-covariance functions. In this report, I present a straightforward method of cosimulation based on the square root of the auto-covariances. The same approach is used for constructing cross-covariance models for the variables. The approach is quite general and does not require symmetry of the cross-covariance. The modelling of the cross-covariance is illustrated with gamma ray and spontaneous potential logs.