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...

coalbed methane (cbm) plays an important role in coal mining safety and natural gas production. in this work, the cbm potential of b2 seam in parvadeh iv coal deposit, in central iran, was evaluated using a combination of local regression and geostatistical methods. as there were 30 sparse methane sampling points in the parvadeh iv coal deposit, no valid variogram was achieved for the methane c...

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 ...

Modelling spatial variability and uncertainty is a highly challenging subject in soiland geosciences. Regression kriging (RK) has several advantages; nevertheless it is not able to model the spatial uncertainty of the target variable. The main aim of this study is to present and test a sequential stochastic simulation approach based on regression kriging (SSSRK), which can be used to generate a...