Results 171 to 180 of about 9,305 (198)
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Semivariogram modeling by weighted least squares
Computers & Geosciences, 1996Abstract Permissible semivariogram models are fundamental for geostatistical estimation and simulation of attributes having a continuous spatiotemporal variation. The usual practice is to fit those models manually to experimental semivariograms.
Xiaodong Jian +2 more
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Semivariogram Models Based on Geometric Offsets
Mathematical Geology, 2006Kriging-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 ...
Michael J. Pyrcz, Clayton V. Deutsch
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Spatio-Temporal Semivariograms as Neighborhood Definers
2022SPATIO-TEMPORAL SEMIVARIOGRAMS AS NEIGHBORHOOD DEFINERS. Brendan J. Hurley George Mason University, 2022 Dissertation Director: Dr. Timothy F. Leslie Spatial neighborhood definitions are a consistent source of disagreement among geographic scholars.
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A Composite Likelihood Approach to Semivariogram Estimation
Journal of Agricultural, Biological, and Environmental Statistics, 1999This article proposes the use of estimating functions based on composite likelihood for the estimation of isotropic as well as geometrically anisotropic semivariogram parameters. The composite likelihood approach is objective, eliminating the specification of distance lags and lag tolerances associated with the commonly used moment estimator ...
Frank C. Curriero, Subhash Lele
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Combining K-means and semivariogram-based grid clustering
47th International Symposium ELMAR, 2005., 2005Clustering is useful in several situations, amongst others: data mining, information retrieval, image segmentation, and data classification. In this paper an approach for grouping data sets that are indexed in the space is proposed. It is based on the k-means algorithm and grid clustering.
M. Trujillo, E. Izquierdo
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The semivariogram in remote sensing: An introduction
Remote Sensing of Environment, 1988Abstract The Earth's surface and remotely sensed imagery contain spatial information that, if quantified, could be used to optimize many sampling procedures in remote sensing. Until recently a suitable and simple technique for the spatial characterisation of surfaces was not readily available.
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Cumulative semivariogram models of regionalized variables
Mathematical Geology, 1989The 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.
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Permeability semivariograms, geological structure, and flow performance
Mathematical Geology, 1996Clastic 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.
J. L. Jensen +3 more
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Semivariogram estimation: asymptotic theory and applications
2016The semivariogram is a function characterizing the second-order dependence structure of an intrinsically stationary random field; its estimation has applications in spatial statistics, particularly in the construction of optimal predictors of the random field at unobserved locations.
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Asymptotic normality of the Nadaraya–Watson semivariogram estimators
TEST, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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