对数泛克立格法在化探中的应用

在霍各乞多金属矿区找矿预测中,利用对数泛克立格法处理1/1万岩石地球化学测量数据资料,将其成果用于成矿条件分析,进行找矿预测.较滑动平均法、趋势面分析法具有估值精度高、系统误差小,且能进行结构分析和给出具体的估值精度等优点,该方法值得推广应用.     

泛克立格法在吉林省某地1：5万化探中的应用及与其它有关方法的对比研究

本文以吉林某地金矿远景区１：５万水系沉积物测量资料为例，比较了目前国内外几种常用化探异常划分方法的优劣性，并推荐在生产实践中使用泛克立格法。     

多元场和泛克立格法在冀东水系沉积物资料处理中的应用

为实现地球化学资料的二次信息开发，用泛克立格法进行地球化学背景面波动条件下的异常圈定；用多元场法定量地表达地球化学异常的元素组合，并根据面金属量反演模型实现金矿资源的定量预测。     

对数泛克立格法在矿产勘查开发中的应用

采用对数泛克立格法对内蒙古乌拉山－大青山分散流资料进行数据处理，缩小了异常范围，突出了异常浓集中心。由于方法考虑到了数据整体的空间位置关系，使异常所处部位更接近矿体的实际位置，为地质找矿优选靶区提供了充分的依据。增强了矿业开发的可信度。     

指示克立格法的理论及方法

地质、物化探数据的分布常出现一个长尾巴,这种分布特征影响了矿产储量计算及数据处理的精度.引起这种非正态分布的因素有:数据中有特异值,它的出现表明矿床具有高品位的矿化作用,这对储量计算及数据处理是很重要的;数据由若干个总体组成,即所谓的混合总体.对这种数据,普通克立格法是不稳健的.为此,本文介绍一种新的地质统计学方法——指示克立格法(非参数方法).文中讨论了(1)指示函数及其二阶矩;(2)指示半变异函数;(3)指示克立格方程组;(4)待估块段品位的指示克立格估计.最后,还介绍1个应用实例,并给出了计算方法和步骤.     

克立格法在采场品位估计中的应用

地下矿山的采场形态变化较大，且不规则，施行克立格法估值有一定的难度。本文从矿化模型的构造入手，了如何直接对地下矿山的采场实施克立格法估值，以便是要场储量，品位和估计精度。     

克立格法在地下水观测网优化设计中的应用

回顾了克立格法在地下水观测网优化设计中的应用和进展,介绍了克立格法的基本原理,论述了克立格法在地下水观测网中的应用思路与实现方法,举例说明了利用克立格法优化调整河北平原地下水观测网。     

Kriging in terms of projections

In the last few years, an increasing number of practical studies using so-called kriging estimation procedures have been published. Various terms, such as universal kriging, lognormal kriging, ordinary kriging, etc., are used to define different estimation procedures, leaving a certain confusion about what kriging really is. The object of this paper is to show what is the common backbone of all these estimation procedures, thus justifying the common name of kriging procedures. The word kriging (in French krigeage) is a concise and convenient term to designate the classical procedure of selecting, within agiven class of possible estimators, the estimator with a minimum estimation variance (i.e., the estimator which leads to a minimum variance of the resulting estimation error). This estimation variance can be seen as a squared distance between the unknown value and its estimator; the process of minimization of this distance can then be seen as the projection of the unknown value onto the space within which the search for an estimator is carried out.     

The Second-Order Stationary Universal Kriging Model Revisited

Universal kriging originally was developed for problems of spatial interpolation if a drift seemed to be justified to model the experimental data. But its use has been questioned in relation to the bias of the estimated underlying variogram (variogram of the residuals), and furthermore universal kriging came to be considered an old-fashioned method after the theory of intrinsic random functions was developed. In this paper the model is reexamined together with methods for handling problems in the inference of parameters. The efficiency of the inference of covariance parameters is shown in terms of bias, variance, and mean square error of the sampling distribution obtained by Monte Carlo simulation for three different estimators (maximum likelihood, bias corrected maximum likelihood, and restricted maximum likelihood). It is shown that unbiased estimates for the covariance parameters may be obtained but if the number of samples is small there can be no guarantee of good estimates (estimates close to the true value) because the sampling variance usually is large. This problem is not specific to the universal kriging model but rather arises in any model where parameters are inferred from experimental data. The validity of the estimates may be evaluated statistically as a risk function as is shown in this paper.     

Simplicial Indicator Kriging

Indicator kriging (IK) is a spatial interpolation technique devised for estimating a conditional cumulative distribution function at an unsampled location. The result is a discrete approximation, and its corresponding estimated probability density function can be viewed as a composition in the simplex. This fact suggested a compositional approach to IK which, by construction, avoids all its standard drawbacks (negative predictions, not-ordered or larger than one). Here, a simple algorithm to develop the procedure is presented.     

Robustness of Kriging weights to non-bias conditions

In this paper, the effect on Kriging weights of non-bias conditions, when the same residual covariance model is used, has been studied by the l₂ norm of the weights difference between Ordinary Kriging and Kriging with a trend model. Four covariance models, in 1-D and 2-D, and in interpolation and extrapolation conditions are examined. Situations in which both algorithms yield the same results are pointed out.     

Compositional Kriging: A Spatial Interpolation Method for Compositional Data

Compositional data are very common in the earth sciences. Nevertheless, little attention has been paid to the spatial interpolation of these data sets. Most interpolators do not necessarily satisfy the constant sum and nonnegativity constraints of compositional data, nor take spatial structure into account. Therefore, compositional kriging is introduced as a straightforward extension of ordinary kriging that complies with these constraints. In two case studies, the performance of compositional kriging is compared with that of the additive logratio-transform. In the first case study, compositional kriging yielded significantly more accurate predictions than the additive logratio-transform, while in the second case study the performances were comparable.     

Comparing some approximate methods for building local confidence intervals for predicting regionalized variables

Approximate local confidence intervals can be produced by nonlinear methods designed to estimate indicator variables. The most precise of these methods, the conditional expectation, can only be used in practice in the multi-Gaussian context. Theoretically, less efficient methods have to be used in more general cases. The methods considered here are indicator kriging, probability kriging (indicator-rank co-kriging), and disjunctive kriging (indicator co-kriging). The properties of these estimators are studied in this paper in the multi-Gaussian context, for this allows a more detailed study than under more general models. Conditional distribution approximation is first studied. Exact results are given for mean squared errors and conditional bias. Then conditional quantile estimators are compared empirically. Finally, confidence intervals are compared from the points of view of bias and precision.     

Rock Kriging with the Microscope

An idea to consider rock textures from a geostatistical viewpoint is suggested. Mineral grains are coded by indicator functions. Four metrics are shown of interest for petrographic applications. The simplest one is used to calculate covariograms of indicators for platinum-bearing gabbronorite from the Pansky rock massif (Kola Peninsula, Russia) with maximal range of 2 units. This is generalized in the concept of a minimal cluster of mineral grains for the given rock. The theory allows us to combine grain-by-grain and cluster-by-cluster considerations of rock texture. It may be used to classify monotonous lithological series using nuances of rock textures.     

An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting

A factorial, computational experiment was conducted to compare the spatial interpolation accuracy of ordinary and universal kriging and two types of inverse squared-distance weighting. The experiment considered, in addition to these four interpolation methods, the effects of four data and sampling characteristics: surface type, sampling pattern, noise level, and strength of small-scale spatial correlation. Interpolation accuracy was measured by the natural logarithm of the mean squared interpolation error. Main effects of all five factors, all two-factor interactions, and several three-factor interactions were highly statistically significant. Among numerous findings, the most striking was that the two kriging methods were substantially superior to the inverse distance weighting methods over all levels of surface type, sampling pattern, noise, and correlation.     

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.     

Efficient updating of kriging estimates and variances

This short note presents a method for efficiently updating ordinary kriging estimates and variances when one or more additional samples are incorporated into the kriging system. First, the foundation linear algebra result is presented. Then the update equations are derived. Finally, an illustrative application of updating is briefly discussed.