Cumulative semivariogram models of regionalized variables

The cumulative semivariogram approach is proposed for modeling regionalized variables in the geological sciences. This semivariogram is defined as the successive summation of half-squared differences which are ranked according to the ascending order of distances extracted from all possible pairs of sample locations within a region. This procedure is useful especially when sampling points are irregularly distributed within the study area. Cumulative semivariograms possess all of the objective properties of classical semivariograms. Classical semivariogram models are evaluated on the basis of the cumulative semivariogram methodology. Model parameter estimation procedures are simplified with the use of arithmetic, semilogarithmic, or double-logarithmic papers. Plots of cumulative semivariogram values vs. corresponding distances may scatter along a straight line on one of these papers, which facilitates model identification as well as parameter estimation. Straight lines are fitted to the cumulative semivariogram scatter diagram by classical linear regression analysis. Finally, applications of the methodology are presented for some groundwater data recorded in the sedimentary basins of the Kingdom of Saudi Arabia.     

Generalized Bootstrap Method for Assessment of?Uncertainty in Semivariogram Inference

The semivariogram and its related function, the covariance, play a central role in classical geostatistics for modeling the average continuity of spatially correlated attributes. Whereas all methods are formulated in terms of the true semivariogram, in practice what can be used are estimated semivariograms and models based on samples. A generalized form of the bootstrap method to properly model spatially correlated data is used to advance knowledge about the reliability of empirical semivariograms and semivariogram models based on a single sample. Among several methods available to generate spatially correlated resamples, we selected a method based on the LU decomposition and used several examples to illustrate the approach. The first one is a synthetic, isotropic, exhaustive sample following a normal distribution, the second example is also a synthetic but following a non-Gaussian random field, and a third empirical sample consists of actual raingauge measurements. Results show wider confidence intervals than those found previously by others with inadequate application of the bootstrap. Also, even for the Gaussian example, distributions for estimated semivariogram values and model parameters are positively skewed. In this sense, bootstrap percentile confidence intervals, which are not centered around the empirical semivariogram and do not require distributional assumptions for its construction, provide an achieved coverage similar to the nominal coverage. The latter cannot be achieved by symmetrical confidence intervals based on the standard error, regardless if the standard error is estimated from a parametric equation or from bootstrap.     

Permeability semivariograms, geological structure, and flow performance

Clastic sediments may have a strong deterministic component to their permeability variation. This structure may be seen in the experimental semivariogram, but published geostatislical studies have not always exploited this feature during data analysis and covariance modeling. In this paper, we describe sedimentary organization, its importance for flow modeling, and how the semivariogram can be used for identification of structure. Clastic sedimentary structure occurs at several scales and is linked to the conditions of deposition. Lamination, bed, and bedset scales show repetitive and trend features that should be sampled carefully to assess the degree of organization and levels of heterogeneity. Interpretation of semivariograms is undertaken best with an appreciation of these geological units und how their features relate to the sampling program. Sampling at inappropriate intervals or with instruments having a large measurement volume, for example, may give misleading semivariograms. Flow simulations for models which include and ignore structure show that the repetitive features in permeability can change anisotropy and recovery performance significantly. If systematic variation is present, careful design of the permeability fields therefore is important particularly to preserve the structure effects.     

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。     

Bayesian Modeling and Inference for Geometrically Anisotropic Spatial Data

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.     

The U-WEDGE Transformation Method for Multivariate Geostatistical Simulation

To speed up multivariate geostatistical simulation it is common to transform the set of attributes into spatially uncorrelated factors that can be simulated independently. Spatial decorrelation methods are usually based on the diagonalisation of the variance/covariance and semivariogram matrices of the set of attributes for a chosen family of lag spacings. These matrices are symmetric and there are several efficient methods for the approximate joint diagonalisation of a family of symmetric matrices. One of these is the uniformly weighted exhaustive diagonalisation with Gauss iterations (U-WEDGE) method. In contrast to the method of minimum/maximum autocorrelation factors (MAF), where a two structure linear model of coregionalisation is assumed, U-WEDGE can be applied directly to the set of experimental semivariogram matrices without having to place restrictions on the number of structures in the linear model of coregionalisation, thus removing one of the restrictions placed on the subsequent modelling of the spatial structure of the factors. We use an iron-ore data set to illustrate the method and present a comparison between the simulated attributes obtained from U-WEDGE and MAF with the full co-simulation of the attributes.     

针对储层裂缝检测的三维P波地震采集设计参数分析

理论和实践表明,垂直定向排列的裂缝能够诱导方位各向异性,三维P波地震是一种经济有效的裂缝勘探方法。合理的三维勘探有利于从三维地震数据中提取和增强有效的属性参数,来表征方位各向异性,达到检测和预测储层裂缝的目的。这里通过综述目前常用的纵波裂缝检测技术,结合部分实际数据,总结了三维P波采集方案设计的一些基本原则,为储层裂缝检测提供了野外设计方面的参考。     

Permutation Methods for Determining the Significance of Spatial Dependence

A class of multivariate nonparametric tests for spatial dependence, Multivariate Sequential Permutation Analyses (MSPA), is developed and applied to the analysis of spatial data. These tests allow the significance level (P value) of the spatial correlation to be computed for each lag class. MSPA is shown to be related to the variogram and other measures of spatial correlation. The interrelationships of these measures of spatial dependence are discussed and the measures are applied to synthetic and real data. The resulting plot of significance level vs. lag spacing, or P-gram, provides insight into the modeling of the semivariogram and the semimADogram. Although the test clearly rejects some models of correlation, the chief value of the test is to quantify the strength of spatial correlation, and to provide evidence that spatial correlation exists     

Semivariogram Models Based on Geometric Offsets

Kriging-based geostatistical models require a semivariogram model. Next to the initial decision of stationarity, the choice of an appropriate semivariogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental semivariograms with a nested combination of proven models such as the spherical, exponential, and Gaussian models. These models work well in most cases; however, there are some shapes found in practice that are difficult to fit. We introduce a family of semivariogram models that are based on geometric shapes, analogous to the spherical semivariogram, that are known to be conditional negative definite and provide additional flexibility to fit semivariograms encountered in practice. A methodology to calculate the associated geometric shapes to match semivariograms defined in any number of directions is presented. Greater flexibility is available through the application of these geometric semivariogram models.     

A parametric study of robustness of kriging variance as a function of range and relative nugget effect for a spherical semivariogram

In geostatistics, an estimation of blocks of a deposit is reported along with the variance of error made in their estimation. This calculation is based on the model chosen for the semivariogram of the deposit so that mistakes in its estimation can manifest themselves in the perception of accuracy with which blocks are known. Changes in kriging variance resulting from various amounts of error in modeling the relative nugget effect and range of the semivariogram are investigated for an extensive set of spherical semivariograms.     

Research on Initial Modeling Method for TTI Anisotropic Parameters Based on Local Tomography

The initial modeling method based on TTI anisotropy can provide an initial model for the fine modeling of tomographic inversion, and the quality of the initial model directly affects the efficiency and accuracy of tomographic inversion. It is necessary to study the accurate initial modeling method of TTI anisotropy. Based on the conventional flow of the initial modeling method of TTI anisotropic, in this paper the anisotropic parameter extraction formula was perfected, a local tomography method to optimize the flow of the initial modeling method of TTI anisotropy was introduced, and the accuracy of the initial model was further improved, which is more consistent with the actual geological conditions and conducive to the fine modeling of the later tomographic inversion. The processing results of model and filed data show that the initial modeling method of TTI anisotropy based on local tomography is effective and practical.     

Spectral Corrected Semivariogram Models

Fitting semivariograms with analytical models can be tedious and restrictive. There are many smooth functions that could be used for the semivariogram; however, arbitrary interpolation of the semivariogram will almost certainly create an invalid function. A spectral correction, that is, taking the Fourier transform of the corresponding covariance values, resetting all negative terms to zero, standardizing the spectrum to sum to the sill, and inverse transforming is a valuable method for constructing valid discrete semivariogram models. This paper addresses some important implementation details and provides a methodology to working with spectrally corrected semivariograms.     

The effect of the nanogranular nature of shale on their poroelastic behavior

Natural composite materials are highly heterogeneous porous materials, with porosities that manifest themselves at scales much below the macroscale of engineering applications. A typical example is shale, the transverse isotropic sealing formation of most hydrocarbon bearing reservoirs. By means of a closed loop approach of microporomechanics modeling, calibration and validation of elastic properties at multiple length scales of shale, we show that the nanogranular nature of this highly heterogeneous material translates into a unique poroelastic signature. The self-consistent scaling of the porous clay stiffness with the clay packing density minimizes the anisotropy of the Biot pore pressure coefficients; whereas the intrinsic anisotropy of the elementary particle translates into a pronounced anisotropy of the Skempton coefficients. This new microporoelasticity model depends only on two shale-specific material parameters which neatly summarize clay mineralogy and bulk density, and which makes the model most appealing for quantitative geomechanics, geophysics and exploitation engineering applications.     

Point Cumulative Semivariogram for Identification of Heterogeneities in Regional Seismicity of Turkey

Geological events are neither isotropic nor homogeneous in their occurrences. These two properties present difficulties for spatial modeling of regionalized variables. This paper presents a point cumulative semivariogram (PCSV) technique for quantifying the heterogeneity characteristics of the phenomenon concerned. The basis of the methodology is to obtain experimental PCSVs for each measurement point which led to estimation of radius of influence around each site. In addition, the experimental PCSVs provide basic information about the heterogeneity of the geological variable in the region, and furthermore many useful interpretations can be made concerning the regional variability of the variable. It provides the measure of cumulative similarity of a regional variable around any measurement site. Because PCSV is a means of measuring total similarity, maps at fixed similarity levels are provided in order to document the regional heterogeneity. Identification of heterogeneities depends on the comparison of fixed PCSV values at a multitude of irregularly scattered sites. The PCSV methodology has been applied to the regional seismic data of Turkey.     

Generalized sequential kriging (GSK) for heterogeneous cell size: property modeling without matrices

Sequential kriging avoids the use of matrices and resolves the issue of unstable solutions. It allows for stepwise ways to get joint estimations and cosimulations that are equivalent to the simultaneous solution. The approach is proposed as the solution for geocellular modeling with variable cell size from heterogeneous structural properties (HSPs) as required for modeling with structural constraints. Rock properties are controlled by structural domains, regions, and structural geology parameters. In some cases, rock properties are cross-correlated to formation thickness, curvature of structures, and other structural attributes. Cell thickness may be proportional to formation thickness and may enter as a conditioning property in the estimation of rock property parameters for simulation. In addition, cell volume controls the upscaling of covariance structures (i.e., regularized variograms). Structural properties are priorly modeled. Perturbation response functions (PRFs) are computed for each cell vs all possible sample point locations to facilitate sequential kriging. Upscaled PRFs are modified following conditional updating after each new data value is included in the estimation of parameters. Generalized sequential kriging is expected to become the main tool for real-time spatial modeling of 3D cellular models with HSP. In addition, some new developments related to the sequential kriging algorithm are included. Sequential kriging can be used for the estimation of parameters for simulation in the so-called unstructured grids.     

Declustering of Clustered Preferential Sampling for?Histogram and Semivariogram Inference

Measurements of attributes obtained more as a consequence of business ventures than sampling design frequently result in samplings that are preferential both in location and value, typically in the form of clusters along the pay. Preferential sampling requires preprocessing for the purpose of properly inferring characteristics of the parent population, such as the cumulative distribution and the semivariogram. Consideration of the distance to the nearest neighbor allows preparation of resampled sets that produce comparable results to those from previously proposed methods. A clustered sampling of size 140, taken from an exhaustive sampling, is employed to illustrate this approach.     

Changes of spatial distributions in copper and lead in Shenzhen surface soil over a 20-year period

Fifty-two surface soil samples from agricultural, forest, and grassland sites were collected from the Shenzhen municipal area for determination of copper and lead levels. The spatial dependence of the measured results was quantified using semivariogram modeling, and structural changes in copper and lead in Shenzhen surface soil were analyzed during the past 20 years from the late 1980s to 2009. The resulting semivariogram direction of copper was from northwest to southwest, while that of lead was from east to northwest.     

On counting the number of data pairs for semivariogram estimation

In planning spatial sampling studies for the purpose of estimating the semivariogram, the number of data pairs separated by a given distance is sometimes used as a comparative index of the precision which can be expected from a given sampling design. Because spatial data are correlated, this index can be unreliable. An alternative index which partially corrects for this correlation, themaximum equivalent uncorrelated pairs, is proposed for comparing spatial designs. The index is developed under the assumption that the underlying stochastic process is Gaussian and is appropriate when the (population) semivariogram is to be estimated by the sample semivariogram.     

Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units

This paper presents a methodology to conduct geostatistical variography and interpolation on areal data measured over geographical units (or blocks) with different sizes and shapes, while accounting for heterogeneous weight or kernel functions within those units. The deconvolution method is iterative and seeks the point-support model that minimizes the difference between the theoretically regularized semivariogram model and the model fitted to areal data. This model is then used in area-to-point (ATP) kriging to map the spatial distribution of the attribute of interest within each geographical unit. The coherence constraint ensures that the weighted average of kriged estimates equals the areal datum.This approach is illustrated using health data (cancer rates aggregated at the county level) and population density surface as a kernel function. Simulations are conducted over two regions with contrasting county geographies: the state of Indiana and four states in the Western United States. In both regions, the deconvolution approach yields a point support semivariogram model that is reasonably close to the semivariogram of simulated point values. The use of this model in ATP kriging yields a more accurate prediction than a naïve point kriging of areal data that simply collapses each county into its geographic centroid. ATP kriging reduces the smoothing effect and is robust with respect to small differences in the point support semivariogram model. Important features of the point-support semivariogram, such as the nugget effect, can never be fully validated from areal data. The user may want to narrow down the set of solutions based on his knowledge of the phenomenon (e.g., set the nugget effect to zero). The approach presented avoids the visual bias associated with the interpretation of choropleth maps and should facilitate the analysis of relationships between variables measured over different spatial supports.     

Seismische Anisotropie des oberen Erdmantels und Intraplatten-Tektonik

Specially planned explosion seismic measurements in the oceans provided conclusive evidence that the velocity of P_n-waves depends on the azimuths of the direction of propagation through the upper mantle. The orientation of this azimuthal anisotropy suggests a close connection with the generation of the oceanic lithosphere: in the Pacific the maximum and minimum velocities are measured in a perpendicular and parallel direction to the axis of the oceanic ridges respectively. The observed anisotropy is so strong that a number of models for the generation of anisotropy can be discarded. The most likely cause is a preferred orientation of minerals. The generation of the anisotropy can be simulated in the laboratory under P-T-conditions of the upper mantle. The influence of the rate of deformation can be studied as well. A recent analysis of explosion seismic data in Southern Germany suggests that the continental upper mantle possesses also a velocity anisotropy dependent on azimuth.