Covariance function diagnostics for spatial linear models

Case deletion diagnostics are developed for detecting observations that are influential in estimating the covariance function of a spatial random field. Diagnostics are developed within the context of universal kriging. Computational formulae are given that make the procedures feasible and the diagnostics are illustrated in an example.     

When do we need a trend model in kriging?

Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option.     

Notes on the robustness of the kriging system

The robustness of the kriging system with respect to uncertainty of the theoretical variogram is investigated. Inequalities for possible changes of the kriging estimator and the estimation variance are derived. Results of a numerical study show that changes of kriging weights can be predicted partly with the help of the maximal kriging weight.     

A spatial estimation model for continuous rock mass characterization from the specific energy of a TBM

Summary  Basic principles of the theory of rock cutting with rolling disc cutters are used to appropriately reduce tunnel boring machine (TBM) logged data and compute the specific energy (SE) of rock cutting as a function of geometry of the cutterhead and operational parameters. A computational code written in Fortran 77 is used to perform Kriging predictions in a regular or irregular grid in 1D, 2D or 3D space based on sampled data referring to rock mass classification indices or TBM related parameters. This code is used here for three purposes, namely: (1) to filter raw data in order to establish a good correlation between SE and rock mass rating (RMR) (or tunnelling quality index Q) along the chainage of the tunnel, (2) to make prediction of RMR, Q or SE along the chainage of the tunnel from boreholes at the exploration phase and design stage of the tunnel, and (3) to make predictions of SE and RMR or Q ahead of the tunnel’s face during excavation of the tunnel based on SE estimations during excavation. The above tools are the basic constituents of an algorithm to continuously update the geotechnical model of the rock mass based on logged TBM data. Several cases were considered to illustrate the proposed methodology, namely: (a) data from a system of twin tunnels in Hong Kong, (b) data from three tunnels excavated in Northern Italy, and (c) data from the section Singuerlin-Esglesias of the Metro L9 tunnel in Barcelona. Correspondence: G. Exadaktylos, Department of Mineral Resources Engineering, Technical University of Crete, Chania, Greece     

Comparing the robustness of ordinary kriging and lognormal kriging: Outlier resistance

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.     

不同采样点数量下土壤有机质含量空间预测方法对比

运用普通克里格、泛克里格、协同克里格和回归克里格4种方法,结合由DEM获取的高程因子以及土壤全氮和阳离子交换量（CEC）,预测了黑龙江省海伦市耕地有机质含量的空间分布。不同样点数量下海伦市土壤有机质含量的空间变异结构分析表明,样点数量多并不一定能够识别土壤有机质含量的结构性连续组分,最优化的布置采样点位置可能比单纯增加...     

Robustness of variograms and conditioning of kriging matrices

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.     

Comparison of approaches to spatial estimation in a bivariate context

The problem of estimating a regionalized variable in the presence of other secondary variables is encountered in spatial investigations. Given a context in which the secondary variable is known everywhere (or can be estimated with great precision), different estimation methods are compared: regression, regression with residual simple kriging, kriging, simple kriging with a mean obtained by regression, kriging with an external drift, and cokriging. The study focuses on 19 pairs of regionalized variables from five different datasets representing different domains (geochemical, environmental, geotechnical). The methods are compared by cross-validation using the mean absolute error as criterion. For correlations between the principal and secondary variable under 0.4, similar results are obtained using kriging and cokriging, and these methods are superior slightly to the other approaches in terms of minimizing estimation error. For correlations greater than 0.4, cokriging generally performs better than other methods, with a reduction in mean absolute errors that can reach 46% when there is a high degree of correlation between the variables. Kriging with an external drift or kriging the residuals of a regression (SKR) are almost as precise as cokriging.     

Evaluation and comparison of spatial interpolators II

The performance of several variations on ordinary kriging and inverse distance estimators is evaluated. Mean squared errors (MSE) were calculated for estimates made on multiple resamplings from five exhaustive data bases representing two distinctly different types of estimation problem. Ordinary kriging, when performed with variograms estimated from the sample data, was more robust than inverse-distance methods to the type of estimation problem, and to the choice of estimation parameters such as number of neighbors.Notice: Although the research described in this article has been funded in part by the United States Environmental Protection Agency through Cooperative Agreement CR818526 to the Harry Reid Center for Environmental Studies, University of Nevada-Las Vegas, it has not been subjected to Agency review. Therefore it does not necessarily reflect the views of the Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

含水层渗透性空间分布的指示克立格估值

详细介绍了指示克立格估值计算的理论和方法。以指示变异函数为基本工具分析了华北某地区第四系含水层渗透性空间分布的结构特征,结果表明该地区含水层渗透性存在明显的各向异性特征。水平方向上,X轴方向的相关性较Y轴方向的好,Z轴的相关性最差。用指示克立格法对未采样点处进行估值,估值结果显示含水层渗透性由山前向滨海逐渐变低,在垂直方向上,渗透性变化不明显,浅部比深部略好;同时给出了估计精度,并认为对估计精度不高的区域可通过增加适当的工程加以控制。最后用交叉验证法对估值结果进行了检验,证明建立的指示变异函数模型合理且估值效果较好。这一实际应用表明指示克立格法可以很好地描述第四系含水层渗透性的空间分布规律。     

An indicator kriging model for investigation of seismic hazard

Time domain probabilistic techniques most often are used for assessment of seismic hazard. Such techniques are based on the historic frequency of ground motion. Hazard is expressed as a probability of experiencing a particular level of seismic activity over a given length of time. One of these techniques utilizes frequency of extreme values for assessment of hazard. The major disadvantage of this technique, however, becomes evident when maximum seismic activity for two consecutive years occurs only a few weeks or months apart. In this case, the extreme value approach overestimates seismic hazard. A new approach for hazard assessment is founded on principles of indicator kriging. This technique evaluates seismic hazard as a simple frequency record, which is more realistic for regions of little to moderate seismicity.     

Estimating spatial distributions of heterogeneous subsurface characteristics by regionalized classification of electrofacies

Regionalized classification of electrofacies utilizes the statistical relationships between laboratory determined hydrologic properties and field-measured geophysical properties to estimate spatial distributions of porosity, permeability, and diagenetic characteristics. The method, illustrated with an application to the St. Peter Sandstone in the Michigan basin, combines techniques for multivariate analysis and spatial estimation. Core plug and borehole geophysical data are clustered into electrofacies that reflect the hydrologic properties and diagenetic characteristics of the formation. Electrofacies characteristics then are used to assign a class membership probability at locations where only geophysical data are available. Three-dimensional estimation of electrofacies occurrence is done by kriging datasets containing the probability of electrofacies membership at borehole locations. The discretization and kriging geometry allow three-dimensional estimation of hydrologic parameters for a large region that incorporates meter-scale heterogeneity. Finally, permeability and porosity are estimated at each grid location by probability-weighting. Because the electrofacies carry information about both the hydrologic and lithologic properties, the resulting spatial distributions provide an understanding of both the present-day flow characteristics and the extent of processes that control them.Managed by Martin Marietta Energy Systems. Inc., under contract DE-AC05-84OR21400 with the U.S. Department of Energy); Publication No. 4371, Environmental Sciences Division, ORNL.     

Geostatistical case studies of the advantages of lognormal-de Wijsian kriging with mean for a base metal mine and a gold mine

Parallel variogram analyses, block kriging, and follow-up studies were effected for the lead content of part of the Prieska copper-zinc ore body and for the gold content of the highly variable Breef in a section of the Loraine gold mine, based first on untransformed values and second on logarithmically transformed values using the lognormal-de Wijsian model. For both models the effect was also analyzed of using the population mean or ignoring it. Practical follow-up comparisons confirm theoretical considerations and show that on these mines conditional biases can be eliminated conveniently by kriging with mean; also that the lognormal-de Wijsian model with mean gives the best results.     

Identifying spatial heterogeneity of coal resource quality in a multilayer coal deposit by multivariate geostatistics

This study presents a new geostatistical approach to characterization of the geometry and quality of a multilayer coal deposit using the data of seam thickness as a geometric property and the contents of ash, sodium, total sulphur, and the heating value as quality properties. A coal deposit in East Kalimantan (Borneo), Indonesia, which has a synclinal geological structure, was chosen as the study site. Semivariogram analysis clarified the strong dependence of heating value on ash content in the top and bottom parts of each seam and the existence of a strong correlation with sodium content over the sub-seams in the same location. The correlations between the geometry and quality of the seams were generally weak. A linear coregionalization model was used to derive the spatial correlation coefficients of two variables at each scale component from the single- and cross-semivariogram matrices. Because the data were correlated spatially in the same seam or over different seams, multivariate techniques (ordinary cokriging and factorial cokriging) were mainly used and the resultant spatial estimates were compared to those derived using a univariate technique (ordinary kriging). A factorial cokriging was effective to decompose the spatial correlation structures with different scales. Another important characteristic was that the sodium content shows distinct segregation: the low zones are concentrated near the boundary of the sedimentary basin, while the high zones are concentrated in the central part. The main component of sodium originates from the abundance of saline water. Therefore, it can be inferred that seawater had stronger effects on the coal depositional process in the central basin than in the border part. The geostatistical modeling results suggest that the thicknesses of all the major seams were controlled by the syncline structure, while the coal qualities chiefly were originated from the coal depositional and diagenetic processes.     

Variogram Fitting by Generalized Least Squares Using an Explicit Formula for the Covariance Structure

In the context of spatial statistics, the classical variogram estimator proposed by Matheron can be written as a quadratic form of the observations. If data are Gaussian with constant mean, then the correlation between the classical variogram estimator at two different lags is a function of the spatial design matrix and the variance matrix. When data are independent with unidimensional and regular support, an explicit formula for this correlation is available. The same is true for a multidimensional and regular support as can be shown by using Kronecker products of matrices. As variogram fitting is a crucial stage for correct spatial prediction, it is proposed to use a generalized least squares method with an explicit formula for the covariance structure (GLSE). A good approximation of the covariance structure is achieved by taking account of the explicit formula for the correlation in the independent situation. Simulations are carried out with several types of underlying variograms, as well as with outliers in the data. Results show that this technique (GLSE), combined with a robust estimator of the variogram, improves the fit significantly.     

Nonparametric estimation of multivariate joint and conditional spatial distributions

To estimate the recoverable reserves of deposits containing more than one metal, linear combinations of sample grades based on the economic importance of each metal have been employed. These linear combinations, called equivalent grades, have inherent problems at least one of which is that the contribution of an individual metal is confounded with all others. As an alternative to equivalent grades, multivariable joint and conditional spatial distributions may be estimated using indicator variable methods. These spatial distributions may then be used to determine the joint or conditional amounts of the metals in the estimated recoverable reserve.This paper was presented at MGUS 87 Conference, Redwood City, California, 14 April 1987.     

On the stability of the geostatistical method

A thorough geostatistical analysis of spatial data, observed at given spatial locations, includes exploratory data analysis, spatial-model building, diagnosing the model fit, and inference on unknown model parameters or unobserved values (at known locations). Using results from mathematical analysis, exact and asymptotic distribution theory, and simulation studies, we argue that, when used sensibly, the geostatistical method is reassuringly stable.     

直线型海洋重力仪负漂移原因分析及处理

分析了直线型KSS31M(SN035)海洋重力仪短期负漂移现象的原因、采取的处理措施和经验总结,认为在减震措施不足的情况下,重力仪长期经受震动,而频繁的搬动会使重力仪弹性系统中的金属弹簧产生较大的弹性疲劳,这种影响在短时间内无法完全消除,由此可能导致月漂移超标;陀螺稳定平台轴因承受震动磨损较严重,轴承间隙变大,导致安装在平台之上的重力传感器经过抛物线测试之后不能完全垂直指向地心,重力在水平方向存在一个微小的分量,最终导致重力读数偏小,即短期负漂移;重力基点环境的变化等对漂移也有一定的影响。建议在搬动重力仪后将仪器保持足够长时间的恒温后再开机,密切监测一段时间,以确保仪器处于良好的工作状态。     

Comparison of two techniques for applying disjunctive kriging: The Gaussian anamorphosis model versus the direct statistical inference of the bivariate distributions

Indicator cokriging is an alternative to disjunctive kriging for estimation of spatial distributions. One way to determine which of these techniques is more accurate for estimation of spatial distributions is to apply each to a particular type of data. A procedure is developed for evaluation of disjunctive kriging and indicator cokriging for such an application. Application of this procedure to earthquake ground motion data found disjunctive kriging to be at least as accurate as indicator cokriging for estimation of spatial distributions of peak horizontal acceleration. Indicator cokriging was superior for all other types of earthquake ground motion data.     

Application of spatial filter theory to kriging

Data-processing requirements for remotely sensed, digital images include spatial filtering to suppress image noise, enhance edges/contacts, and improve image clarity. Spatial filter theory demonstrates that the addition of a high-pass filtered image to a low-pass filtered image yields the original digital image. Application of this principle in kriging can be accomplished by using the same covariance matrix to solve for two weighting vectors to yield a result analogous to low- and high-pass filtering. The addition of kriged estimates calculated using both weighting vectors is analogous to summing high-, and low-pass filtered digital images. This modified method of kriging yields estimates associated with less smoothing compared to ordinary kriging. Statistical moments of original sample data are better preserved through estimation by this method.