二态序列变差函数的定理及初步地质应用

提出并证明了一个关于二态序列变差函数的定理,即二元平稳标准化正态序列的变差函数与其以零为水平的0—1二元序列变差函数的关系式。该定理为通过对定性地质变量的空间序列变异性研究以达到对原定量地质变量空间变异性的研究提供了可能与方便。文中进行了数字仿真分析,并提供了一个地质方面的初步应用。     

基于地质统计学方法的孔压静力触探锥尖阻力固有空间变异性研究

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。     

变差函数在赣杭构造带火山岩型铀矿田(床)评价中的应用

在简要介绍变差函数的原理、算法和研究区地质情况的基础上,将变差函数引入火山岩型铀成矿作用与地形相关性研究中。详细描述了赣杭构造带中不同成矿规模矿田(床)区地形高程变差统计的过程,初步分析了变差统计结果的地质意义。研究结果表明,地形高程值变差函数的长轴方向可以反映研究区的主要构造方向,变差函数的长短轴半径与铀矿床的成矿规模在一定程度上具有对应关系。     

地质统计学变差函数人机对话拟合

变差函数的研究在地质统计学中具有十分重要作用，本文运用界面图形图像处理强的Ｃ￣（＋＋）语言实现了界面友好汉化人机对话变差函数的拟合，主要包括管理菜单的生成，实验变差值的求解，变差图的图形显示，标准函数模型的计算及变差函数人机对话求解等部分。最后对比一下回归分析与人机对话拟合结果。     

地质统计学变差函数人机对话拟合

变差函数的研究在地质统计学中具有十分重要作用，本文运用界面图形图像处理强的Ｃ＾＋＋语言实现了界面友好汉化人机对话变差函数的拟合，主要包括管理菜单的生成，实验变差值的求解，变差图的图形显示，标准函数模型的计算及变差函数人机对话求解等部分，最后对比一下回归分析与人机对话拟合结果。     

变差函数球状模型的自动拟合与实现

根据变差函数的随机性和空间结构性,综合利用变差函数计算方法和加权线性规划拟合方法,分别拟合出各主要方向上的模型参数,再根据各向异性情况进行结构套合,实现了变差函数的计算及球状模型的自动拟合.针对样本中存在特异值的情况,算法中还提供了相对变差函数方法,有效地抑制了特异值对变差函数的影响,保证了球状模型拟合的精度.本算法在VC++6.0中实现,利用拟合出的模型,对样本区域进行插值得到网格文件,调用Surfer8.0绘制了等值线图.通过交叉验证和综合法验证,表明拟合度较高.     

不同地质统计方法在确定渗透系数场中的对比研究

应用地质统计方法研究渗透系数场的空间变异性。利用MMR含水层场地实测数据,通过去类分析、特异值处理、正态变换,逐步逼近研究区渗透系数的稳健变差函数,得到三维渗透系数场的几何各向异性套合模型。在此基础上,采用普通克里格法和指示克里格法、高斯序列模拟法和指示序列模拟法分别对数据进行插值和条件模拟。最后结合具体的地质条件,对四种方法在渗透系数场生成中的应用进行对比分析和评价。     

冻土水盐时空变异性条件模拟的初步研究

本文用地质统计学的变差函数讨论了试验区冻融土中不同时期非饱和带土壤垂向水分和盐分的时空变异性;运用条件模拟理论(简称CS)用较少的采样点再现其水盐的波动过程,并同普通Kriging最优插值法进行了对比分析和评价;同时研究了影响条件模拟Monte-Carlo试验计算精度和效率的两个重要因素--随机数种子和随机数序列长度的取值规律,得到了比较平稳的取值范围.     

策克白垩纪承压水含水层渗透系数变化规律

以渗透系数的对数作为区域化变量,计算0度、90度方向的试验变差函数,并进行理论变差函数模型拟合,所得结果用来分析策克地区白垩纪承压含水层空间结构并判定含水层可能的各向异性。变差函数模型经交互验证后,再采用普通克里格法进行二维空间插值获得该含水层的渗透系数二维分布。利用地质剖面资料、岩土样分析成果、岩相古地理分析成果对插值得到的二维对数渗透系数等值线图进行验证。     

胜坨油田二区三角洲砂岩油藏井间储层参数预测

论述退火模拟方法及其在胜坨油田二区的应用,包括空间结构分析、变差函数计算、退火模拟预测等内容。分析了三角洲储层参数变差函数特征,预测了储层参数分布。研究结果与储层沉积学研究结果吻合,得到开发生产实际检验。     

Directional Wavelets and a Wavelet Variogram for Two-Dimensional Data

This paper describes two new approaches that can be used to compute the two-dimensional experimental wavelet variogram. They are based on an extension from earlier work in one dimension. The methods are powerful 2D generalizations of the 1D variogram that use one- and two-dimensional filters to remove different types of trend present in the data and to provide information on the underlying variation simultaneously. In particular, the two-dimensional filtering method is effective in removing polynomial trend with filters having a simple structure. These methods are tested with simulated fields and microrelief data, and generate results similar to those of the ordinary method of moments variogram. Furthermore, from a filtering point of view, the variogram can be viewed in terms of a convolution of the data with a filter, which is computed fast in O(NLogN) number of operations in the frequency domain. We can also generate images of the filtered data corresponding to the nugget effect, sill and range of the variogram. This in turn provides additional tools to analyze the data further.     

Estimating Variogram Uncertainty

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.     

Wavelets and the Generalization of the Variogram

The experimental variogram computed in the usual way by the method of moments and the Haar wavelet transform are similar in that they filter data and yield informative summaries that may be interpreted. The variogram filters out constant values; wavelets can filter variation at several spatial scales and thereby provide a richer repertoire for analysis and demand no assumptions other than that of finite variance. This paper compares the two functions, identifying that part of the Haar wavelet transform that gives it its advantages. It goes on to show that the generalized variogram of order k=1, 2, and 3 filters linear, quadratic, and cubic polynomials from the data, respectively, which correspond with more complex wavelets in Daubechies's family. The additional filter coefficients of the latter can reveal features of the data that are not evident in its usual form. Three examples in which data recorded at regular intervals on transects are analyzed illustrate the extended form of the variogram. The apparent periodicity of gilgais in Australia seems to be accentuated as filter coefficients are added, but otherwise the analysis provides no new insight. Analysis of hyerpsectral data with a strong linear trend showed that the wavelet-based variograms filtered it out. Adding filter coefficients in the analysis of the topsoil across the Jurassic scarplands of England changed the upper bound of the variogram; it then resembled the within-class variogram computed by the method of moments. To elucidate these results, we simulated several series of data to represent a random process with values fluctuating about a mean, data with long-range linear trend, data with local trend, and data with stepped transitions. The results suggest that the wavelet variogram can filter out the effects of long-range trend, but not local trend, and of transitions from one class to another, as across boundaries.     

FuzzyKrig: a comprehensive matlab toolbox for geostatistical estimation of imprecise information

Using kriging has been accepted today as the most common method of estimating spatial data in such different fields as the geosciences. To be able to apply kriging methods, it is necessary that the data and variogram model parameters be precise. To utilize the imprecise (fuzzy) data and parameters, use is made of fuzzy kriging methods. Although it has been 30 years since different fuzzy kriging algorithms were proposed, its use has not become as common as other kriging methods (ordinary, simple, log, universal, etc.); lack of a comprehensive software that can perform, based on different fuzzy kriging algorithms, the related calculations in a 3D space can be the main reason. This paper describes an open-source software toolbox (developed in Matlab) for running different algorithms proposed for fuzzy kriging. It also presents, besides a short presentation of the fuzzy kriging method and introduction of the functions provided by the FuzzyKrig toolbox, 3 cases of the software application under the conditions where: 1) data are hard and variogram model parameters are fuzzy, 2) data are fuzzy and variogram model parameters are hard, and 3) both data and variogram model parameters are fuzzy.     

Variance–Covariance Matrix of the Experimental Variogram: Assessing Variogram Uncertainty

Assessment of the sampling variance of the experimental variogram is an important topic in geostatistics as it gives the uncertainty of the variogram estimates. This assessment, however, is repeatedly overlooked in most applications mainly, perhaps, because a general approach has not been implemented in the most commonly used software packages for variogram analysis. In this paper the authors propose a solution that can be implemented easily in a computer program, and which, subject to certain assumptions, is exact. These assumptions are not very restrictive: second-order stationarity (the process has a finite variance and the variogram has a sill) and, solely for the purpose of evaluating fourth-order moments, a Gaussian distribution for the random function. The approach described here gives the variance–covariance matrix of the experimental variogram, which takes into account not only the correlation among the experiemental values but also the multiple use of data in the variogram computation. Among other applications, standard errors may be attached to the variogram estimates and the variance–covariance matrix may be used for fitting a theoretical model by weighted, or by generalized, least squares. Confidence regions that hold a given confidence level for all the variogram lag estimates simultaneously have been calculated using the Bonferroni method for rectangular intervals, and using the multivariate Gaussian assumption for K-dimensional elliptical intervals (where K is the number of experimental variogram estimates). A general approach for incorporating the uncertainty of the experimental variogram into the uncertainty of the variogram model parameters is also shown. A case study with rainfall data is used to illustrate the proposed approach.     

Nonstationarity of the Mean and Unbiased Variogram Estimation: Extension of the Weighted Least-Squares Method

When concerned with spatial data, it is not unusual to observe a nonstationarity of the mean. This nonstationarity may be modeled through linear models and the fitting of variograms or covariance functions performed on residuals. Although it usually is accepted by authors that a bias is present if residuals are used, its importance is rarely assessed. In this paper, an expression of the variogram and the covariance function is developed to determine the expected bias. It is shown that the magnitude of the bias depends on the sampling configuration, the importance of the dependence between observations, the number of parameters used to model the mean, and the number of data. The applications of the expression are twofold. The first one is to evaluate a priori the importance of the bias which is expected when a residuals-based variogram model is used for a given configuration and a hypothetical data dependence. The second one is to extend the weighted least-squares method to fit the variogram and to obtain an unbiased estimate of the variogram. Two case studies show that the bias can be negligible or larger than 20%. The residual-based sample variogram underestimates the total variance of the process but the nugget variance may be overestimated.     

Distance-gradient-based variogram and Kriging to evaluate cobalt-rich crust deposits on seamounts

The spatial distribution of cobalt-rich crust thicknesses on seamounts is partly controlled by water depth and slope gradients. Conventional distance–direction-based variogram have not effectively expressed the spatial self-correlation or anisotropy of the thicknesses of cobalt-rich crusts. To estimate resources in cobalt-rich crusts on seamounts using geostatistics, we constructed a new variogram model to adapt to the spatial distribution of the thicknesses of the cobalt-rich crusts. In this model, we defined the data related to cobalt-rich crusts on seamounts as three-dimensional surface random variables, presented an experimental variogram process based on the distance–gradient or distance–“relative water depth,” and provided a theoretical variogram model that follows this process. This method was demonstrated by the spatial estimation of the thicknesses of cobalt-rich crusts on a seamount, and the results indicated that the new variogram model reflects the spatial self-correlation of the thicknesses of cobalt-rich crusts well. Substituted into the Kriging equation, the new variogram model successfully estimated the spatial thickness distribution of these cobalt-rich crusts.     

Uncertainty in fractal dimension estimated from power spectra and variograms

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.     

Some Results on the Spatial Breakdown Point of Robust Point Estimates of the Variogram

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.     

New variogram modeling method using MGGP and SVR

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.