共查询到20条相似文献，搜索用时 31 毫秒
1.
Multivariate Spatial Modeling for Geostatistical Data Using Convolved Covariance Functions 总被引：1，自引：0，他引：1
Soil pollution data collection typically studies multivariate measurements at sampling locations, e.g., lead, zinc, copper
or cadmium levels. With increased collection of such multivariate geostatistical spatial data, there arises the need for flexible
explanatory stochastic models. Here, we propose a general constructive approach for building suitable models based upon convolution
of covariance functions. We begin with a general theorem which asserts that, under weak conditions, cross convolution of covariance
functions provides a valid cross covariance function. We also obtain a result on dependence induced by such convolution. Since,
in general, convolution does not provide closed-form integration, we discuss efficient computation.
We then suggest introducing such specification through a Gaussian process to model multivariate spatial random effects within
a hierarchical model. We note that modeling spatial random effects in this way is parsimonious relative to say, the linear
model of coregionalization. Through a limited simulation, we informally demonstrate that performance for these two specifications
appears to be indistinguishable, encouraging the parsimonious choice. Finally, we use the convolved covariance model to analyze
a trivariate pollution dataset from California. 相似文献
2.
Although there are multiple methods for modeling matrix covariance functions and matrix variograms in the geostatistical literature, the linear coregionalization model is still widely used. In particular it is easy to check to ensure whether the matrix covariance function is positive definite or that the matrix variogram is conditionally negative definite. One of the difficulties in using a linear coregionalization model is in determining the number of basic structures and the corresponding covariance functions or variograms. In this paper, a new procedure is given for identifying the basic structures of the space–time linear coregionalization model and modeling the matrix variogram. This procedure is based on the near simultaneous diagonalization of the sample matrix variograms computed for a set of spatiotemporal lags. A case study using a multivariate spatiotemporal data set provided by the Environmental Protection Agency of Lombardy, Italy, illustrates how nearly simultaneous diagonalization of the empirical matrix variograms simplifies modeling of the matrix variograms. The new methodology is compared with a previous one by analyzing various indices and statistics. 相似文献
3.
Alexandre Anozé Emerick 《Computational Geosciences》2016,20(1):35-47
Ensemble-based methods are becoming popular assisted history matching techniques with a growing number of field applications. These methods use an ensemble of model realizations, typically constructed by means of geostatistics, to represent the prior uncertainty. The performance of the history matching is very dependent on the quality of the initial ensemble. However, there is a significant level of uncertainty in the parameters used to define the geostatistical model. From a Bayesian viewpoint, the uncertainty in the geostatistical modeling can be represented by a hyper-prior in a hierarchical formulation. This paper presents the first steps towards a general parametrization to address the problem of uncertainty in the prior modeling. The proposed parametrization is inspired in Gaussian mixtures, where the uncertainty in the prior mean and prior covariance is accounted by defining weights for combining multiple Gaussian ensembles, which are estimated during the data assimilation. The parametrization was successfully tested in a simple reservoir problem where the orientation of the major anisotropic direction of the permeability field was unknown. 相似文献
4.
I. C. Lemmer 《Mathematical Geosciences》1986,18(7):589-604
For any distribution of grades, a particular cutoff grade is shown here to exist at which the indicator covariance is proportional to the grade covariance to a very high degree of accuracy. The name “mononodal cutoff” is chosen to denote this grade. Its importance for robust grade variography in the presence of a large coefficient of variation—typical of precious metals—derives from the fact that the mononodal indicator variogram is then linearly related to the grade variogram yet is immune to outlier data and is found to be particularly robust under data information reduction. Thus, it is an excellent substitute to model in lieu of a difficult grade variogram. A theoretical expression for the indicator covariance is given as a double series of orthogonal polynomials that have the grade density function as weight function. Leading terms of this series suggest that indicator and grade covariances are first-order proportional, with cutoff grade dependence being carried by the proportionality factor. Kriging equations associated with this indicator covariance lead to cutoff-free kriging weights that are identical to grade kriging weights. This circumstance simplifies indicator kriging used to estimate local point-grade histograms, while at the same time obviating order relations problems. 相似文献
5.
Ronald Christensen 《Mathematical Geology》1990,22(6):655-664
A proof is provided that the predictions obtained from kriging based on intrinsic random functions of orderk are identical to those obtained from anappropriate universal kriging model. This is a theoretical result based on known variability measures. It does not imply that people performing traditional universal kriging will get the same predictions as those using intrinsic random functions, because traditionally these methods differ in how variability is modeled. For intrinsic random functions, the same proof shows that predictions do not depend on the specific choice of the generalized covariance function. It is argued that the choice between these methods is really one of modeling and estimating the variability in the data. 相似文献
6.
This paper is devoted to a geostatistical attempt at modeling migration errors when localizing a reflector in the ground. Starting with a probabilistic velocity model and choosing the simple geometrical optics background for the wave propagation in such media, we give the expression of the errors. This may be quantified provided the covariance of the velocity field is known. Variance of arrival times at constant offset is related to the covariance of the velocity field at hand. A practical application is given in the same paragraph. After that we give a typical schema for migration and uncertainty modeling: starting with seismic data, we make the weak seismic inversion. We then obtain the covariance of the velocity field that we use for simulating migration errors. The main issues of this methodology are discussed in the last paragraph. 相似文献
7.
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
8.
I. C. Lemmer 《Mathematical Geology》1986,18(7):589-604
For any distribution of grades, a particular cutoff grade is shown here to exist at which the indicator covariance is proportional to the grade covariance to a very high degree of accuracy. The name mononodal cutoff is chosen to denote this grade. Its importance for robust grade variography in the presence of a large coefficient of variation—typical of precious metals—derives from the fact that the mononodal indicator variogram is then linearly related to the grade variogram yet is immune to outlier data and is found to be particularly robust under data information reduction. Thus, it is an excellent substitute to model in lieu of a difficult grade variogram. A theoretical expression for the indicator covariance is given as a double series of orthogonal polynomials that have the grade density function as weight function. Leading terms of this series suggest that indicator and grade covariances are first-order proportional, with cutoff grade dependence being carried by the proportionality factor. Kriging equations associated with this indicator covariance lead to cutoff-free kriging weights that are identical to grade kriging weights. This circumstance simplifies indicator kriging used to estimate local point-grade histograms, while at the same time obviating order relations problems.This paper is based in part on a PhD thesis submitted to the Department of Applied Earth Sciences, Stanford University, Stanford, California 94305, in 1984 (unpublished). 相似文献
9.
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
10.
J. R. Carr 《Geotechnical and Geological Engineering》1990,8(4):393-399
Summary A crucial concern when implementing computer algorithms for geostatistical analyses on microcomputers is speed of execution. Kriging, in particular, typically relies on a Gauss elimination algorithm to solve for weights. Because such an alogrithm is required for each estimate, the solution for weights can result in very slow program execution speed on a microcomputer. One approach to enhancing the efficiency of Gauss elimination is demonstrated herein. The upper triangle plus diagonal of the intersample covariance matrix is used in a modified banded Gauss elimination algorithm. Results show that such an algorithm yields approximately a two-fold reduction in execution time for kriging when the number of nearest neighbours used for estimation is large. 相似文献
11.
A. G. Journel 《Mathematical Geology》1999,31(8):955-964
Markov models based on various data screening hypotheses are often used because they reduce the statistical inference burden. In the case of co-located cokriging, the commonly used Markov model results in the cross-covariance being proportional to the primary covariance. Such model is inappropriate in the presence of a smoothly varying secondary variable defined on a much larger volume support than the primary variable. For such cases, an alternative Markov screening hypothesis is proposed that results in a more continuous cross-covariance proportional to the secondary covariance model. A parallel development of both Markov models is presented. A companion paper provides a comparative application to a real data set. 相似文献
12.
The variogram is a critical input to geostatistical studies: (1) it is a tool to investigate and quantify the spatial variability of the phenomenon under study, and (2) most geostatistical estimation or simulation algorithms require an analytical variogram model, which they will reproduce with statistical fluctuations. In the construction of numerical models, the variogram reflects some of our understanding of the geometry and continuity of the variable, and can have a very important impact on predictions from such numerical models. The principles of variogram modeling are developed and illustrated with a number of practical examples. A three-dimensional interpretation of the variogram is necessary to fully describe geologic continuity. Directional continuity must be described simultaneously to be consistent with principles of geological deposition and for a legitimate measure of spatial variability for geostatistical modeling algorithms. Interpretation principles are discussed in detail. Variograms are modeled with particular functions for reasons of mathematical consistency. Used correctly, such variogram models account for the experimental data, geological interpretation, and analogue information. The steps in this essential data integration exercise are described in detail through the introduction of a rigorous methodology. 相似文献
13.
Teacher''s Aide Variogram Interpretation and Modeling 总被引：13，自引：0，他引：13
The variogram is a critical input to geostatistical studies: (1) it is a tool to investigate and quantify the spatial variability of the phenomenon under study, and (2) most geostatistical estimation or simulation algorithms require an analytical variogram model, which they will reproduce with statistical fluctuations. In the construction of numerical models, the variogram reflects some of our understanding of the geometry and continuity of the variable, and can have a very important impact on predictions from such numerical models. The principles of variogram modeling are developed and illustrated with a number of practical examples. A three-dimensional interpretation of the variogram is necessary to fully describe geologic continuity. Directional continuity must be described simultaneously to be consistent with principles of geological deposition and for a legitimate measure of spatial variability for geostatistical modeling algorithms. Interpretation principles are discussed in detail. Variograms are modeled with particular functions for reasons of mathematical consistency. Used correctly, such variogram models account for the experimental data, geological interpretation, and analogue information. The steps in this essential data integration exercise are described in detail through the introduction of a rigorous methodology. 相似文献
14.
Reservoir characterization needs the integration of various data through history matching, especially dynamic information
such as production or 4D seismic data. Although reservoir heterogeneities are commonly generated using geostatistical models,
random realizations cannot generally match observed dynamic data. To constrain model realizations to reproduce measured dynamic
data, an optimization procedure may be applied in an attempt to minimize an objective function, which quantifies the mismatch
between real and simulated data. Such assisted history matching methods require a parameterization of the geostatistical model
to allow the updating of an initial model realization. However, there are only a few parameterization methods available to
update geostatistical models in a way consistent with the underlying geostatistical properties. This paper presents a local
domain parameterization technique that updates geostatistical realizations using assisted history matching. This technique
allows us to locally change model realizations through the variation of geometrical domains whose geometry and size can be
easily controlled and parameterized. This approach provides a new way to parameterize geostatistical realizations in order
to improve history matching efficiency. 相似文献
15.
William E. Miller 《Mathematical Geology》2002,34(3):275-290
An alternative definition of distance is presented for observations plotted in a ternary diagram and, more generally, for observations in a compositional data set. This definition, which conforms to the triangular coordinate system of the ternary diagram, is compared to other distance measures, and is shown to be tied to the covariance structure of compositional data. Angular differences are also discussed briefly in an Appendix. 相似文献
16.
Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore–Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data. 相似文献
17.
Conditional Simulation of Random Fields with Bivariate Gamma Isofactorial Distributions 总被引：5，自引：0，他引：5
Xavier Emery 《Mathematical Geology》2005,37(4):419-445
This work focuses on a random function model with gamma marginal and bivariate isofactorial distributions, which has been applied in mining geostatistics for estimating recoverable reserves by disjunctive kriging. The objective is to widen its use to conditional simulation and further its application to the modeling of continuous attributes in geosciences. First, the main properties of the bivariate gamma isofactorial distributions are analyzed, with emphasis in the destructuring of the extreme values, the presence of a proportional effect (higher variability in high-valued areas), and the asymmetry in the spatial correlation of the indicator variables with respect to the median threshold. Then, we provide examples of stationary random functions with such bivariate distributions, for which the shape parameter of the marginal distribution is half an integer. These are defined as the sum of squared independent Gaussian random fields. An iterative algorithm based on the Gibbs sampler is proposed to perform the simulation conditional to a set of existing data. Such ‘multivariate chi-square’ model generalizes the well-known multigaussian model and is more flexible, since it allows defining a shape parameter which controls the asymmetry of the marginal and bivariate distributions. 相似文献
18.
Evaluation of ground water quality using multiple linear regression and structural equation modeling 总被引：7，自引：4，他引：3
I. Chenini S. Khemiri 《International Journal of Environmental Science and Technology》2009,6(3):509-519
A methodology for characterizing ground water quality of watersheds using hydrochemical data that mingle multiple linear regression and structural equation modeling is presented. The aim of this work is to analyze hydrochemical data in order to explore the compositional of phreatic aquifer groundwater samples and the origin of water mineralization, using mathematical method and modeling, in Maknassy Basin, central Tunisia). Principal component analysis is used to determine the sources of variation between parameters. These components show that the variations within the dataset are related to variation in sulfuric acid and bicarbonate, sodium and cloride, calcium and magnesium which are derived from water-rock interaction. Thus, an equation is explored for the sampled ground water. Using Amos software, the structural equation modeling allows, to test in simultaneous analysis the entire system of variables (sodium, magnesium, sulfat, bicarbonate, cloride, calcium), in order to determine the extent to which it is consistent with the data. For this purpose, it should investigate simultaneously the interactions between the different components of ground water and their relationship with total dissolved solids. The integrated result provides a method to characterize ground water quality using statistical analyses and modeling of hydrochemical data in Maknassy basin to explain the ground water chemistry origin. 相似文献
19.
Lin Y. Hu 《Mathematical Geology》2002,34(8):953-963
Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters. 相似文献
20.
In subsurface flow modeling, compositional simulation is often required to model complex recovery processes, such as gas/CO 2 injection. However, compositional simulation on fine-scale geological models is still computationally expensive and even prohibitive. Most existing upscaling techniques focus on black-oil models. In this paper, we present a general framework to upscale two-phase multicomponent flow in compositional simulation. Unlike previous studies, our approach explicitly considers the upscaling of flow and thermodynamics. In the flow part, we introduce a new set of upscaled flow functions that account for the effects of compressibility. This is often ignored in the upscaling of black-oil models. In the upscaling of thermodynamics, we show that the oil and gas phases within a coarse block are not at chemical equilibrium. This non-equilibrium behavior is modeled by upscaled thermodynamic functions, which measure the difference between component fugacities among the oil and gas phases. We apply the approach to various gas injection problems with different compositional features, permeability heterogeneity, and coarsening ratios. It is shown that the proposed method accurately reproduces the averaged fine-scale solutions, such as component overall compositions, gas saturation, and density solutions in the compositional flow. 相似文献