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...

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...

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...

Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a sche...

The benefits of an integrated geographical information system (GIS) and a geostatistics approach to accurately model the spatial distribution pattern of precipitation are known. However, the determination of the most appropriate geostatistical algorithm for each case is usually neglected, i.e. it is important to select the best interpolation technique for each study area to obtain accurate resu...

Metamodeling has been widely used for design optimization by building surrogate models for computationally intensive engineering application problems. Among all the metamodeling methods, the kriging method has gained significant interest for its accuracy.However, in traditional krigingmethods, themean structure is constructed using a fixed set of polynomial basis functions, and the optimization...

We present a method for efficiently fitting a time series of spatial functions to observed data. The method is closely related to kriging, which is an interpolation method based on a stochastic data model. While kriging is effective and versatile for estimating individual functions from observed data, it must be extended to incorporate temporal correlation. In this paper, we introduce temporal ...