Kriging is a powerful spatial interpolation technique, especially for irregularly spaced data points, and is widely used throughout the earth and environmental sciences. The estimation at an unsampled location is given as the weighted sum of the circumjacent observed points. The weighting factors depend on a model of spatial correlation. Calculation of the weighting factors is done by minimizin...

We consider the problem of the interpolation of irregularly spaced spatial data, applied to observation of Cosmic Microwave Background (CMB) anisotropies. The well-known interpolation methods and kriging are compared to the binning method which serves as a reference approach. We analyse kriging versus binning results for different resolutions and noise level in the original data. Most of the ti...

Due to substantial effect of classification of resource models on future mine planning, one should come with an accurate method of estimation to guarantee that the minimum error is acquired in the estimation process. The known world class Cu-Mo deposit, Sarcheshmeh Porphyry deposit (central Iran) selected as the study area. The Hypogene zone of the deposit was chosen as the space in which estim...

and Salas (1985) compared kriging with several other interpolation techniques, including inverse distance, for The choice of an optimal interpolation technique for estimating annual precipitation distributions and found kriging to soil properties at unsampled locations is an important issue in sitebe superior to inverse distance weighting. Warrick et specific management. The objective of this s...

The co-simulation is the chosen method within the ESPRIT Project INSYDE for the validation of hybrid SDL and VHDL specifications. This paper presents a concept for the integrated SDL and VHDL simulator and its realization in C++. The INSYDE concept for the co-simulation is based on the connection of two existing commercial simulators by the Coupling Module. The Integrated Simulator presented in...

Recent advancements in remote sensing technology and the increasing size of satellite constellations allow for massive geophysical information to be gathered daily on a global scale by numerous platforms different fidelity. The auto-regressive co-kriging model provides suitable framework analysis such data sets as it is able account cross-dependencies among fidelity outputs. However, its implem...

Following the Gauss-Markov theorem the generalized lest-squares estimator is the best linear unbiased estimator but following the kriging theory its use is limited. This paper shows the kriging constraint on the classic generalized least-squares estimator.

Spatial information surveyed by photogrammetry, airborne LiDAR and Mobile Measurement System (MMS) above ground level can be analyzed by scientists using standard geostatistical methodologies such as ordinary Kriging and sequential Gaussian simulation to interpolate heterogeneities of profiles from sparse sample data. Proven effective by researchers, the Kriging algorithm model is used by comme...