共查询到20条相似文献，搜索用时 687 毫秒
1.
Scale Matching with Factorial Kriging for Improved Porosity Estimation from Seismic Data 总被引：1，自引：0，他引：1
When seismic data and porosity well logs contain information at different spatial scales, it is important to do a scale-matching of the datasets. Combining different data types with scale mismatch can lead to suboptimal results. A good correlation between seismic velocity and rock properties provides a basis for integrating seismic data in the estimation of petrophysical properties. Three-dimensional seismic data provides an unique exhaustive coverage of the interwell reservoir region not available from well data. However, because of the limitations of measurement frequency bandwidth and view angles, the seismic image can not capture the true seismic velocity at all spatial scales present in the earth. The small-scale spatial structure of heterogeneities may be absent in the measured seismic data. In order to take best advantage of the seismic data, factorial kriging is applied to separate the small and large-scale structures of both porosity and seismic data. Then the spatial structures in seismic data which are poorly correlated with porosity are filtered out prior to integrating seismic data into porosity estimation. 相似文献
2.
Eulogio Pardo-Igúzquiza 《Mathematical Geology》1999,31(1):47-65
This paper shows the application of the Bayesian inference approach in estimating spatial covariance parameters. This methodology is particularly valuable where the number of experimental data is small, as occurs frequently in modeling reservoirs in petroleum engineering or when dealing with hydrodynamic variables in groundwater hydrology. There are two main advantages of Bayesian estimation: firstly that the complete distribution of the parameters is estimated and, from this distribution, it is a straightforward procedure to obtain point estimates, confidence regions, and interval estimates; secondly, all the prior information about the parameters (information available before the data are collected) is included in the inference procedure through their prior distribution. The results obtained from simulation studies are discussed. 相似文献
3.
Geostatistical analysis of spatial random functions frequently uses sample variograms computed from increments of samples of a regionalized random variable. This paper addresses the theory of computing variograms not from increments but from spatial variances. The objective is to extract information about the point support space from the average or larger support data. The variance is understood as a parametric and second moment average feature of a population. However, it is well known that when the population is for a stationary random function, spatial variance within a region is a function of the size and geometry of the region and not a function of location. Spatial variance is conceptualized as an estimation variance between two physical regions or a region and itself. If such a spatial variance could be measured within several sizes of windows, such variances allow the computation of the sample variogram. The approach is extended to covariances between attributes that lead to the cross-variogram. The case of nonpoint sample support of the blocks or elements composing each window is also included. A numerical example illustrates the application of this conceptualization. 相似文献
4.
The majority of geostatistical estimation and simulation algorithms rely on a covariance model as the sole characteristic of the spatial distribution of the attribute under study. The limitation to a single covariance implicitly calls for a multivariate Gaussian model for either the attribute itself or for its normal scores transform. The Gaussian model could be justified on the basis that it is both analytically simple and it is a maximum entropy model, i.e., a model that minimizes unwarranted structural properties. As a consequence, the Gaussian model also maximizes spatial disorder (beyond the imposed covariance) which can cause flow simulation results performed on multiple stochastic images to be very similar; thus, the space of response uncertainty could be too narrow entailing a misleading sense of safety. The ability of the sole covariance to adequately describe spatial distributions for flow studies, and the assumption that maximum spatial disorder amounts to either no additional information or a safe prior hypothesis are questioned. This paper attempts to clarify the link between entropy and spatial disorder and to provide, through a detailed case study, an appreciation for the impact of entropy of prior random function models on the resulting response distributions. 相似文献
5.
The majority of geostatistical estimation and simulation algorithms rely on a covariance model as the sole characteristic of the spatial distribution of the attribute under study. The limitation to a single covariance implicitly calls for a multivariate Gaussian model for either the attribute itself or for its normal scores transform. The Gaussian model could be justified on the basis that it is both analytically simple and it is a maximum entropy model, i.e., a model that minimizes unwarranted structural properties. As a consequence, the Gaussian model also maximizes spatial disorder (beyond the imposed covariance) which can cause flow simulation results performed on multiple stochastic images to be very similar; thus, the space of response uncertainty could be too narrow entailing a misleading sense of safety. The ability of the sole covariance to adequately describe spatial distributions for flow studies, and the assumption that maximum spatial disorder amounts to either no additional information or a safe prior hypothesis are questioned. This paper attempts to clarify the link between entropy and spatial disorder and to provide, through a detailed case study, an appreciation for the impact of entropy of prior random function models on the resulting response distributions. 相似文献
6.
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
7.
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
8.
Scales of Reservoir Heterogeneities and Impact of Seismic Resolution on Geostatistical Integration 总被引：1，自引：0，他引：1
Seismic measurements may be used in geostatistical techniques for estimation and simulation of petrophysical properties such as porosity. The good correlation between seismic and rock properties provides a basis for these techniques. Seismic data have a wide spatial coverage not available in log or core data. However, each seismic measurement has a characteristic response function determined by the source-receiver geometry and signal bandwidth. The image response of the seismic measurement gives a filtered version of the true velocity image. Therefore the seismic image cannot reflect exactly the true seismic velocity at all scales of spatial heterogeneities present in the Earth. The seismic response function can be approximated conveniently in the spatial spectral domain using the Born approximation. How the seismic image response affects the estimation of variogram. and spatial scales and its impact on geostatistical results is the focus of this paper. Limitations of view angles and signal bandwidth not only smooth the seismic image, increasing the variogram range, but also can introduce anisotropic spatial structures into the image. The seismic data are enhanced by better characterizing and quantifying these attributes. As an exercise, examples of seismically assisted cokriging and cosimulation of porosity between wells are presented. 相似文献
9.
George Christakos 《Mathematical Geology》1985,17(5):489-515
Parameter estimation has become increasingly interesting the last few decades for a variety of engineering topics. In such situations, one may face problems like (a) how to estimate parameters for which erroneous measurements are available (direct estimation), or (b) how to estimate coefficients of some process model governing a geological phenomenon when these coefficients are inaccessible or difficult to access by direct investigation (inverse estimation or identification). Both these problems are examined in this presentation from a modern stochastic viewpoint, where parameters sought are interpretated mathematically as random functions, generated and estimated in space or time with the aid of recursive models. Advantages of this methodology are remarkable, from both theoretical and physical points of view, as compared to conventional statistics or nonrecursive estimators. Particularly it may offer more accurate estimators, better representation of spatial variation, and a means of overcoming difficulties such as excessive computational time or computer storage. To test effectiveness of this type of estimation, a series of representative case studies from geotechnical practice have been computed in detail. 相似文献
10.
受工程勘察成本及试验场地限制,可获得的试验数据通常有限,基于有限的试验数据难以准确估计岩土参数统计特征和边坡可靠度。贝叶斯方法可以融合有限的场地信息降低对岩土参数不确定性的估计进而提高边坡可靠度水平。但是,目前的贝叶斯更新研究大多假定参数先验概率分布为正态、对数正态和均匀分布,似然函数为多维正态分布,这种做法的合理性有待进一步验证。总结了岩土工程贝叶斯分析常用的参数先验概率分布及似然函数模型,以一个不排水黏土边坡为例,采用自适应贝叶斯更新方法系统探讨了参数先验概率分布和似然函数对空间变异边坡参数后验概率分布推断及可靠度更新的影响。计算结果表明：参数先验概率分布对空间变异边坡参数后验概率分布推断及可靠度更新均有一定的影响,选用对数正态和极值I型分布作为先验概率分布推断的参数后验概率分布离散性较小。选用Beta分布和极值I型分布获得的边坡可靠度计算结果分别偏于保守和危险,选用对数正态分布获得的边坡可靠度计算结果居中。相比之下,似然函数的影响更加显著。与其他类型似然函数相比,由多维联合正态分布构建的似然函数可在降低对岩土参数不确定性估计的同时,获得与场地信息更为吻合的计算结果。另外,构建似然函数时不同位置处测量误差之间的自相关性对边坡后验失效概率也具有一定的影响。 相似文献
11.
Eulogio Pardo-Igúzquiza 《Mathematical Geology》1998,30(1):95-108
In this paper, the maximum likelihood method for inferring the parameters of spatial covariances is examined. The advantages of the maximum likelihood estimation are discussed and it is shown that this method, derived assuming a multivariate Gaussian distribution for the data, gives a sound criterion of fitting covariance models irrespective of the multivariate distribution of the data. However, this distribution is impossible to verify in practice when only one realization of the random function is available. Then, the maximum entropy method is the only sound criterion of assigning probabilities in absence of information. Because the multivariate Gaussian distribution has the maximum entropy property for a fixed vector of means and covariance matrix, the multinormal distribution is the most logical choice as a default distribution for the experimental data. Nevertheless, it should be clear that the assumption of a multivariate Gaussian distribution is maintained only for the inference of spatial covariance parameters and not necessarily for other operations such as spatial interpolation, simulation or estimation of spatial distributions. Various results from simulations are presented to support the claim that the simultaneous use of maximum likelihood method and the classical nonparametric method of moments can considerably improve results in the estimation of geostatistical parameters. 相似文献
12.
Sample schemes used in geostatistical surveys must be suitable for both variogram estimation and kriging. Previously schemes
have been optimized for one of these steps in isolation. Ordinary kriging generally requires the sampling locations to be
evenly dispersed over the region. Variogram estimation requires a more irregular pattern of sampling locations since comparisons
must be made between measurements separated by all lags up to and beyond the range of spatial correlation. Previous studies
have not considered how to combine these optimized schemes into a single survey and how to decide what proportion of sampling
effort should be devoted to variogram estimation and what proportion devoted to kriging
An expression for the total error in a geostatistical survey accounting for uncertainty due to both ordinary kriging and variogram
uncertainty is derived. In the same manner as the kriging variance, this expression is a function of the variogram but not
of the sampled response data. If a particular variogram is assumed the total error in a geostatistical survey may be estimated
prior to sampling. We can therefore design an optimal sample scheme for the combined processes of variogram estimation and
ordinary kriging by minimizing this expression. The minimization is achieved by spatial simulated annealing. The resulting
sample schemes ensure that the region is fairly evenly covered but include some close pairs to analyse the spatial correlation
over short distances. The form of these optimal sample schemes is sensitive to the assumed variogram. Therefore a Bayesian
approach is adopted where, rather than assuming a single variogram, we minimize the expected total error over a distribution
of plausible variograms. This is computationally expensive so a strategy is suggested to reduce the number of computations
required 相似文献
13.
A geometrically anisotropic spatial process can be viewed as being a linear transformation of an isotropic spatial process. Customary semivariogram estimation techniques often involve ad hoc selection of the linear transformation to reduce the region to isotropy and then fitting a valid parametric semivariogram to the data under the transformed coordinates. We propose a Bayesian methodology which simultaneously estimates the linear transformation and the other semivariogram parameters. In addition, the Bayesian paradigm allows full inference for any characteristic of the geometrically anisotropic model rather than merely providing a point estimate. Our work is motivated by a dataset of scallop catches in the Atlantic Ocean in 1990 and also in 1993. The 1990 data provide useful prior information about the nature of the anisotropy of the process. Exploratory data analysis (EDA) techniques such as directional empirical semivariograms and the rose diagram are widely used by practitioners. We recommend a suitable contour plot to detect departures from isotropy. We then present a fully Bayesian analysis of the 1993 scallop data, demonstrating the range of inferential possibilities. 相似文献
14.
基于改进混沌果蝇优化小波阈值法地震信号随机噪声压制 总被引：1，自引：0，他引：1
由于野外采集地震资料往往带有较多的随机噪声,给资料解释造成困难。针对小波阈值去噪的阈值选取通常需要对信号进行先验估计,带有较强猜测性,阈值选取难以获得最优结果。本文提出基于改进混沌果蝇优化的小波阈值法,将基于广义交叉验证(GCV)函数设定为阈值选取目标函数,在混沌果蝇优化算法中引入调节系数实现对该目标函数的迭代寻优,在无先验信息前提下,获取最优小波阈值。通过将本文算法用于合成地震记录和实际地震记录进行去噪处理,并对比常用小波阈值去噪算法,证明了本文算法的有效性。 相似文献
15.
Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation 总被引：1，自引：0，他引：1
Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility
to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated
using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making.
One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable,
joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty
and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility
resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km2 (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical
and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio
(alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte
Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes
from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through
joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated
for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer
the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised
by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision
of K value predictions. 相似文献
16.
Mine gas disaster prediction and prevention are based on gas content measurement, which results in initial stage loss when determining coal gas desorption contents in engineering applications. We propose a Bayesian probability statistical method in the coal gas desorption model on the basis of constrained prior information. First, we use a self-made coal sample gas desorption device to test initial stage gas desorption data of tectonic coal and undeformed coal. Second, we calculate the initial stage loss of different coal samples with the power exponential function parameters by using Bayesian probability statistics and least squares estimation. Results show that Bayesian probability statistics and least squares estimation can be used to obtain regression and desorption coefficients, thereby illustrating the Bayesian estimation method’s validity and reliability. Given that the Bayesian probability method can apply prior information to constrain the model’s posterior parameters, it provides results that are statistically significant in the initial stage loss of coal gas desorption by connecting observation data and prior information.
相似文献17.
A methodology was presented for observation-based settlement prediction with consideration of the spatial correlation structure of soil. The spatial correlation is introduced among the settlement model parameters and the settlements at various points are spatially correlated through these geotechnical parameters, which naturally describe the phenomenon. The method is based on Bayesian estimation by considering both prior information, including spatial correlation and observed settlement, to search for the best estimates of the parameters at any arbitrary points on the ground. Within the Bayesian framework, the optimised selection of auto-correlation distance by Akaike's Bayesian Information Criterion (ABIC) is also proposed. The application of the proposed approach in consolidation settlement prediction using Asaoka's method is presented in this paper. Several case studies were carried out using simulated settlement data to investigate the performance the proposed approach. It is concluded that the accuracy of the settlement prediction can be improved by taking into account the spatial correlation structure and the proposed approach gives the rational prediction of the settlement at any location at any time with quantified uncertainty. 相似文献
18.
Spatial data availability and its implications for sustainable development of the Brazilian Amazon 总被引：1，自引：0，他引：1
In the Amazonia region, general policy based on sustainable development must be adopted to counterbalance natural conservation and the needs of people. To discuss and monitor the regional development requires a significant variety of spatial data. However, making data available is not simple and various solutions usually are presented in non-standardized and non-interchangeable databases. This paper presents an overview of spatial data available for the Amazonia region, and initiatives of data dissemination systems, considering various geographical scales and data access. From this general perspective, we discuss some ideas about the importance of spatial data for sustainability and suggest interactive systems that could contribute to local development and environmental conservation. The limitation on the promotion of spatial information and data sharing in Amazonia is neither computational nor technological. Rather, it is a matter of informing those who generate the data about the benefits of knowledge as a public good and promoting collaborative work. Creating a convergent spatial consciousness about the territory, and bringing together every environmental player, is a step towards the discussion and planning of the sustainable development of the Amazonia. 相似文献
19.
A Methodology for Establishing a Data Reliability Measure for Value of Spatial Information Problems 总被引：2，自引：0，他引：2
We propose a value of information (VOI) methodology for spatial Earth problems. VOI is a tool to determine whether purchasing
a new information source would improve a decision-makers’ chances of taking the optimal action. A prior uncertainty assessment
of key geologic parameters and a reliability of the data to resolve them are necessary to make a VOI assessment. Both of these
elements are challenging to obtain, as this assessment is made before the information is acquired. We present a flexible prior
geologic uncertainty modeling scheme that allows for the inclusion of many types of spatial parameter. Next, we describe how
to obtain a physics-based reliability measure by simulating the geophysical measurement on the generated prior models and
interpreting the simulated data. Repeating this simulation and interpretation for all datasets, a frequency table can be obtained
that describes how many times a correct or false interpretation was made by comparing them to their respective original model.
This frequency table is the reliability measure and allows a more realistic VOI calculation. An example VOI calculation is
demonstrated for a spatial decision related to aquifer recharge where two geophysical techniques are considered for their
ability to resolve channel orientations. As necessitated by spatial problems, this methodology preserves the structure, influence
and dependence of spatial variables through the prior geological modeling and the explicit geophysical simulation and interpretations. 相似文献
20.
在重要的岩土工程中,确定大样本岩土参数的概率分布对岩土工程稳定性和可靠性分析有着极其重要的意义。为此,基于积分均方误差最小计算得到的最优窗宽,提出了推断大样本岩土参数概率密度函数的正态信息扩散法。该方法基于信息扩散原理,从试验样本和信息论的角度出发,充分利用样本提供的数据信息,而不是先假定成经典的概率分布曲线拟合检验,数学意义和物理意义更加充分和严密。以推断压缩指数Cc的概率密度函数为例,分析了窗宽对信息扩散估计结果的影响规律,说明了该方法在大样本岩土参数概率密度函数推断方面的合理性。用该方法推断了大样本岩土抗剪强度参数的概率密度函数,通过K-S检验,验证了该方法的正确性和实用性。 相似文献