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1.
Dae S. Young 《Mathematical Geology》1987,19(6):467-479
Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators: kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics to spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings. 相似文献
2.
Olivier Dubrule 《Mathematical Geosciences》2017,49(4):441-465
Many variogram (or covariance) models that are valid—or realizable—models of Gaussian random functions are not realizable indicator variogram (or covariance) models. Unfortunately there is no known necessary and sufficient condition for a function to be the indicator variogram of a random set. Necessary conditions can be easily obtained for the behavior at the origin or at large distance. The power, Gaussian, cubic or cardinal-sine models do not fulfill these conditions and are therefore not realizable. These considerations are illustrated by a Monte Carlo simulation demonstrating nonrealizability over some very simple three-point configurations in two or three dimensions. No definitive result has been obtained about the spherical model. Among the commonly used models for Gaussian variables, only the exponential appears to be a realizable indicator variogram model in all dimensions. It can be associated with a mosaic, a Boolean or a truncated Gaussian random set. In one dimension, the exponential indicator model is closely associated with continuous-time Markov chains, which can also lead to more variogram models such as the damped oscillation model. One-dimensional random sets can also be derived from renewal processes, or mosaic models associated with such processes. This provides an interesting link between the geostatistical formalism, focused mostly on two-point statistics, and the approach of quantitative sedimentologists who compute the probability distribution function of the thickness of different geological facies. The last part of the paper presents three approaches for obtaining new realizable indicator variogram models in three dimensions. One approach consists of combining existing realizable models. Other approaches are based on the formalism of Boolean random sets and truncated Gaussian functions. 相似文献
3.
4.
Comparing the robustness of ordinary kriging and lognormal kriging: Outlier resistance 总被引：1，自引：0，他引：1
Ordinary kriging is well-known to be optimal when the data have a multivariate normal distribution (and if the variogram is known), whereas lognormal kriging presupposes the multivariate lognormality of the data. But in practice, real data never entirely satisfy these assumptions. In this article, the sensitivity of these two kriging estimators to departures from these assumptions and in particular, their resistance to outliers is considered. An outlier effect index designed to assess the effect of a single outlier on both estimators is proposed, which can be extended to other types of estimators. Although lognormal kriging is sensitive to slight variations in the sill of the variogram of the logs (i.e., their variance), it is not influenced by the estimate of the mean of the logs.This paper was presented at MGUS 87 Conference, Redwood City, California, 14 April 1987. 相似文献
5.
Robustness of variograms and conditioning of kriging matrices 总被引：1，自引：0，他引：1
Current ideas of robustness in geostatistics concentrate upon estimation of the experimental variogram. However, predictive algorithms can be very sensitive to small perturbations in data or in the variogram model as well. To quantify this notion of robustness, nearness of variogram models is defined. Closeness of two variogram models is reflected in the sensitivity of their corresponding kriging estimators. The condition number of kriging matrices is shown to play a central role. Various examples are given. The ideas are used to analyze more complex universal kriging systems.Research performed while on leave at Centre de Geóstatistique et de Morphologie Mathématique, Fontainebleau. 相似文献
6.
Changik Han Jiyang Wang Mingguo Zheng Ende Wang Jianming Xia GwangSu Li Sunchol Choe 《Earth Science Informatics》2016,9(2):197-213
A critical step for kriging in geostatistics is estimation of the variogram. Traditional variogram modeling comprise of the experimental variogram calculation, appropriate variogram model selection and model parameter determination. Selecting of the variogram model and fitting of model parameters is the most controversial aspect of geostatistics. Shapes of valid variogram models are finite, and sometimes, the optimal shape of the model can not be fitted, leading to reduced estimation accuracy. In this paper, a new method is presented to automatically construct a model shape and fit model parameters to experimental variograms using Support Vector Regression (SVR) and Multi-Gene Genetic Programming (MGGP). The proposed method does not require the selection of a variogram model and can directly provide the model shape and parameters of the optimal variogram. The validity of the proposed method is demonstrated in a number of cases. 相似文献
7.
When estimating the mean value of a variable, or the total amount of a resource, within a specified region it is desirable to report an estimated standard error for the resulting estimate. If the sample sites are selected according to a probability sampling design, it usually is possible to construct an appropriate design-based standard error estimate. One exception is systematic sampling for which no such standard error estimator exists. However, a slight modification of systematic sampling, termed 2-step tessellation stratified (2TS) sampling, does permit the estimation of design-based standard errors. This paper develops a design-based standard error estimator for 2TS sampling. It is shown that the Taylor series approximation to the variance of the sample mean under 2TS sampling may be expressed in terms of either a deterministic variogram or a deterministic covariance function. Variance estimation then can be approached through the estimation of a variogram or a covariance function. The resulting standard error estimators are compared to some more traditional variance estimators through a simulation study. The simulation results show that estimators based on the new approach may perform better than traditional variance estimators. 相似文献
8.
This paper is concerned with vector random fields on spheres with second-order increments, which are intrinsically stationary and mean square continuous and have isotropic variogram matrix functions. A characterization of the continuous and isotropic variogram matrix function on a sphere is derived, in terms of an infinite sum of the products of positive definite matrices and ultraspherical polynomials. It is valid for Gaussian or elliptically contoured vector random fields, but may not be valid for other non-Gaussian vector random fields on spheres such as a χ 2, log-Gaussian, or skew-Gaussian vector random field. Some parametric variogram matrix models are derived on spheres via different constructional approaches. A simulation study is conducted to illustrate the implementation of the proposed model in estimation and cokriging, whose performance is compared with that using the linear model of coregionalization. 相似文献
9.
Highly Robust Variogram Estimation 总被引：5，自引：0，他引：5
Marc G. Genton 《Mathematical Geology》1998,30(2):213-221
The classical variogram estimator proposed by Matheron is not robust against outliers in the data, nor is it enough to make simple modifications such as the ones proposed by Cressie and Hawkins in order to achieve robustness. This paper proposes and studies a variogram estimator based on a highly robust estimator of scale. The robustness properties of these three estimators are analyzed and compared. Simulations with various amounts of outliers in the data are carried out. The results show that the highly robust variogram estimator improves the estimation significantly. 相似文献
10.
Before optimal linear prediction can be performed on spatial data sets, the variogram is usually estimated at various lags and a parametric model is fitted to those estimates. Apart from possible a priori knowledge about the process and the user's subjectivity, there is no standard methodology for choosing among valid variogram models like the spherical or the exponential ones. This paper discusses the nonparametric estimation of the variogram and its derivative, based on the spectral representation of positive definite functions. The use of the estimated derivative to help choose among valid parametric variogram models is presented. Once a model is selected, its parameters can be estimated—for example, by generalized least squares. A small simulation study is performed that demonstrates the usefulness of estimating the derivative to help model selection and illustrates the issue of aliasing. MATLAB software for nonparametric variogram derivative estimation is available at http://www-math.mit.edu/~gorsich/derivative.html. An application to the Walker Lake data set is also presented. 相似文献
11.
Covariance and variogram functions have been extensively studied in Euclidean space. In this article, we investigate the validity
of commonly used covariance and variogram functions on the sphere. In particular, we show that the spherical and exponential
models, as well as power variograms with 0<α≤1, are valid on the sphere. However, two Radon transforms of the exponential model, Cauchy model, the hole-effect model and
power variograms with 1<α≤2 are not valid on the sphere. A table that summarizes the validity of commonly used covariance and variogram functions on
the sphere is provided. 相似文献
12.
On a controversial method for modeling a coregionalization 总被引：2，自引：0，他引：2
P. Goovaerts 《Mathematical Geology》1994,26(2):197-204
This paper reviews two alternative approaches for modeling the (cross) variograms in a coregionalization: (1) fitting the traditional linear model of coregionalization. or (2) deducing the crossvariogram model as a linear combination of prior direct (auto) variogram models while checking the Cauchy-Schwarz inequalities. We show that the second approach has no practical advantage over the traditional one and may not be valid if more than two variables are involved. In such case. Cauchy-Schwarz inequalities are necessary but not sufficient conditions for validity of a coregionalization model. 相似文献
13.
普朗铜矿床铜品位分布地质统计学研究 总被引：5，自引：0，他引：5
运用地质统计学的方法对普朗矿区铜品位分布情况进行了研究,建立了铜品位变异规律的数学模型,分别得到了矿体厚度、倾向和走向3个方向的变异函数,该函数呈几何异向性,比值为1:1.612:3.869,反映出矿体铜品位在3个方向上的相关性较好,说明铜品位分布总体变化系数不大,有利于矿山的开采.统计得出铜品位属于正态分布,表明下一步用普通克立格法进行估值效果最好,为矿山规划设计和生产提供了理论依据. 相似文献
14.
R. M. Lark 《Mathematical Geosciences》2008,40(7):729-751
The effect of outliers on estimates of the variogram depends on how they are distributed in space. The ‘spatial breakdown
point’ is the largest proportion of observations which can be drawn from some arbitrary contaminating process without destroying
a robust variogram estimator, when they are arranged in the most damaging spatial pattern. A numerical method is presented
to find the spatial breakdown point for any sample array in two dimensions or more. It is shown by means of some examples
that such a numerical approach is needed to determine the spatial breakdown point for two or more dimensions, even on a regular
square sample grid, since previous conjectures about the spatial breakdown point in two dimensions do not hold. The ‘average
spatial breakdown point’ has been used as a basis for practical guidelines on the intensity of contaminating processes that
can be tolerated by robust variogram estimators. It is the largest proportion of contaminating observations in a data set
such that the breakdown point of the variance estimator used to obtain point estimates of the variogram is not exceeded by
the expected proportion of contaminated pairs of observations over any lag. In this paper the behaviour of the average spatial
breakdown point is investigated for cases where the contaminating process is spatially dependent. It is shown that in two
dimensions the average spatial breakdown point is 0.25. Finally, the ‘empirical spatial breakdown point’, a tool for the exploratory
analysis of spatial data thought to contain outliers, is introduced and demonstrated using data on metal content in the soils
of Sheffield, England. The empirical spatial breakdown point of a particular data set can be used to indicate whether the
distribution of possible contaminants is likely to undermine a robust variogram estimator. 相似文献
15.
濮城沙三中油藏具有两个主物源,分别为NE向与SE向。油藏数值模拟需要在一套地质网格中对其进行模拟。经典的地质统计学利用变差函数描述区域化变量的空间几何结构特性。变差函数的计算是基于两点进行统计的,对其描述主要涉及方位角、变程、块金值和基台值。为了在一套模拟网格中模拟出多个物源条件下储层的分布特征,必须在不同的位置设置不同的变差函数参数。文中给出了两种方法实现这一目的:一是采用人为分区,把不同物源影响的区域分成不同的区块,分别对不同的区块设置不同的变差函数参数;二是采用变方位角,即根据不同的位置设置不同的变差函数方位角。这两种方法都实现了在一套网格中模拟具有多个物源方向的储层分布,更真实地再现了储层的空间展布特征。 相似文献
16.
Estimation of non-ergodic variograms and their sampling variance by design-based sampling strategies
Design-based sampling strategies based on classical sampling theory offer unprecedented potentials for estimation of non-ergodic variograms. Unbiased and uncorrelated estimates of the semivariance at the selected lags and of its sampling variance can be simply obtained. These estimates are robust against deviations from an assumed spatial autocorrelation model. The same holds for the variogram model parameters and their sampling (co)variances. Moreover, an objective measure for lack of fit of the fitted model can simply be derived. The estimators for two basic sampling designs, simple random sampling and stratified simple random sampling of pairs of points, are presented. The first has been tested in real world for estimating the non-ergodic variograms of three soil properties. The parameters of variogram models and their sampling (co)variances were estimated with 72 pairs of points distributed over six lags. 相似文献
17.
The reliability of using fractal dimension (D) as a quantitative parameter to describe geological variables is dependent mainly
on the accuracy of estimated D values from observed data. Two widely used methods for the estimation of fractal dimensions
are based on fitting a fractal model to experimental variograms or power-spectra on a log-log plot. The purpose of this paper
is to study the uncertainty in the fractal dimension estimated by these two methods. The results indicate that both spectrum
and variogram methods result in biased estimates of the D value. Fractal dimension calculated by these two methods for the
same data will be different unless the bias is properly corrected. The spectral method results in overestimated D values.
The variogram method has a critical fractal dimension, below which overestimation occurs and above which underestimation occurs.
On the bases of 36,000 simulated realizations we propose empirical formulae to correct for biases in the spectral and variogram
estimated fractal dimension. Pitfalls in estimating fractal dimension from data contaminated by white noise or data having
several fractal components have been identified and illustrated by simulated examples. 相似文献
18.
On the Use of Non-Euclidean Distance Measures in Geostatistics 总被引：4，自引：0，他引：4
Frank C. Curriero 《Mathematical Geology》2006,38(8):907-926
In many scientific disciplines, straight line, Euclidean distances may not accurately describe proximity relationships among
spatial data. However, non-Euclidean distance measures must be used with caution in geostatistical applications. A simple
example is provided to demonstrate there are no guarantees that existing covariance and variogram functions remain valid (i.e.
positive definite or conditionally negative definite) when used with a non-Euclidean distance measure. There are certain distance
measures that when used with existing covariance and variogram functions remain valid, an issue that is explored. The concept
of isometric embedding is introduced and linked to the concepts of positive and conditionally negative definiteness to demonstrate
classes of valid norm dependent isotropic covariance and variogram functions, results many of which have yet to appear in
the mainstream geostatistical literature or application. These classes of functions extend the well known classes by adding
a parameter to define the distance norm. In practice, this distance parameter can be set a priori to represent, for example,
the Euclidean distance, or kept as a parameter to allow the data to choose the metric. A simulated application of the latter
is provided for demonstration. Simulation results are also presented comparing kriged predictions based on Euclidean distance
to those based on using a water metric. 相似文献
19.
Estimating Variogram Uncertainty 总被引：10，自引：0，他引：10
The variogram is central to any geostatistical survey, but the precision of a variogram estimated from sample data by the method of moments is unknown. It is important to be able to quantify variogram uncertainty to ensure that the variogram estimate is sufficiently accurate for kriging. In previous studies theoretical expressions have been derived to approximate uncertainty in both estimates of the experimental variogram and fitted variogram models. These expressions rely upon various statistical assumptions about the data and are largely untested. They express variogram uncertainty as functions of the sampling positions and the underlying variogram. Thus the expressions can be used to design efficient sampling schemes for estimating a particular variogram. Extensive simulation tests show that for a Gaussian variable with a known variogram, the expression for the uncertainty of the experimental variogram estimate is accurate. In practice however, the variogram of the variable is unknown and the fitted variogram model must be used instead. For sampling schemes of 100 points or more this has only a small effect on the accuracy of the uncertainty estimate. The theoretical expressions for the uncertainty of fitted variogram models generally overestimate the precision of fitted parameters. The uncertainty of the fitted parameters can be determined more accurately by simulating multiple experimental variograms and fitting variogram models to these. The tests emphasize the importance of distinguishing between the variogram of the field being surveyed and the variogram of the random process which generated the field. These variograms are not necessarily identical. Most studies of variogram uncertainty describe the uncertainty associated with the variogram of the random process. Generally however, it is the variogram of the field being surveyed which is of interest. For intensive sampling schemes, estimates of the field variogram are significantly more precise than estimates of the random process variogram. It is important, when designing efficient sampling schemes or fitting variogram models, that the appropriate expression for variogram uncertainty is applied. 相似文献
20.
Pierre Petitgas Mathieu Woillez Mathieu Doray Jacques Rivoirard 《Mathematical Geosciences》2018,50(2):187-208
Marine research survey data on fish stocks often show a small proportion of very high-density values, as for many environmental data. This makes the estimation of second-order statistics, such as the variance and the variogram, non-robust. The high fish density values are generated by fish aggregative behaviour, which may vary greatly at small scale in time and space. The high values are thus imprecisely known, both in their spatial occurrence and order of magnitude. To map such data, three indicator-based geostatistical methods were considered, the top-cut model, min–max autocorrelation factors (MAF) of indicators, and multiple indicator kriging. In the top-cut and MAF approaches, the variable is decomposed into components and the most continuous ones (those corresponding to the low and medium values) are used to guide the mapping. The methods are proposed as alternatives to ordinary kriging when the variogram is difficult to estimate. The methods are detailed and applied on a spatial data set of anchovy densities derived from a typical fish stock acoustic survey performed in the Bay of Biscay, which show a few high-density values distributed in small spatial patches and also as solitary events. The model performances are analyzed by cross-validating the data and comparing the kriged maps. Results are compared to ordinary kriging as a base case. The top-cut model had the best cross-validation performance. The indicator-based models allowed mapping high-value areas with small spatial extent, in contrast to ordinary kriging. Practical guidelines for implementing the indicator-based methods are provided. 相似文献