Kriging and Variogram Models
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Cited by (28)
RKPCA-based approach for fault detection in large scale systems using variogram method
2022, Chemometrics and Intelligent Laboratory SystemsGeostatistical analysis of uranium concentrations in North-Western part of Ogun State, Nigeria
2021, Journal of Environmental RadioactivityAssessing population vulnerability towards summer energy poverty: Case studies of Madrid and London
2019, Energy and BuildingsCitation Excerpt :For Madrid, the kriging geostatistical analysis method from ArcGIS 10.5 software was used to interpolate between the computed CDHs at each sensor location. The variogram was set to be exponential, as it has been widely used in meteorology and contamination prediction and it gave a smaller mean-squared error than the spherical and the Gaussian variograms [61–63]. To make data comparable between cities, values were classified into deciles, with the first decile representing the highest temperature values and hence the most severe weather conditions.
Correlation between Mo mineralization and faults using geostatistical and fractal modeling in porphyry deposits of Kerman Magmatic Belt, SE Iran
2017, Journal of Geochemical ExplorationCitation Excerpt :The variograms and anisotropic ellipsoid are important tools for spatial analysis in the geostatistical methods (David, 1970; Journel and Huijbregts, 1978; Deutsch and Journel, 1998; Davis, 2002; Pyrcz and Deutsch, 2014). These are fundamental in geostatistical interpretation because directions of different ranges for a regional variables (e.g., ore grades) are determined, and also the main trends of geological particulars can be delineated (VerHoef and Cressie, 1993; Calder and Cressie, 2009). In this study, variography and creation of anisotropic ellipsoid were carried out for Mo, Cu and FD by RockWorks 15 software package.
Estimating gasoline performance in internal combustion engines with simulation metamodels
2017, FuelCitation Excerpt :Kriging allows unbiased spatial predictions at any position in a domain by linear interpolation of observed points with minimum variance. It is usually designated as Best Linear Unbiased Estimators (BLUE) [49–53]. The fundamental concept of kriging is the spatial dependence.