Mapping of groundwater level observations often makes very little use of auxiliary data and is often undertaken simply by manual interpolation or ordinary kriging of the heads. Recently, a number of geostatistical methods have emerged that significantly improve estimates by incorporating the land surface elevation and groundwater flow or drawdown equations. However, at the regional scale heads ...

Some nondestructive techniques of the Structural Health Monitoring (SHM) have improved their analysis in past decades. Among them, electromechanical impedance-based SHM technique (EMI-SHM) has been tested several fields and associated to different statistical methodologies. Considering nature spatial variation damage metric data along structures, herein is proposed use indicator kriging method ...

This study describes a method used to improve ore grade estimation in a limestone deposit in India. Ore grade estimation for the limestone deposit was complicated by the complex lithological structure of the deposit. The erratic nature of the deposit and the unavailability of adequate samples for each of the lithogical units made standard geostatistical methods of capturing the spatial variatio...

Ž . Using spatial simulated annealing SSA , spatial sampling schemes can be optimised for minimal kriging variance. Two optimisation criteria are presented in this paper. The first criterion minimises the average kriging variance, the second the maximum kriging variance. In a simple case with 23 observations, performances of a sampling scheme obtained with SSA were compared with performances of...

Kriging is a spatial prediction method, which can predict at any location and return a measure of prediction confidence (the kriging standard error). There exist many variations of kriging, some of which can be complex, especially those that allow many of its parameters to vary spatially. To calibrate such a kriging model and to be able to interpret its results can therefore be quite daunting. ...

This survey considers the optimization of simulated systems. The simulation may be either deterministic or random. The survey reflects the author’s extensive experience with simulationoptimization through Kriging (or Gaussian process) metamodels using a frequentist (non-Bayesian) approach. The analysis of Kriging metamodels may use bootstrapping. The survey discusses both parametric bootstrappi...

The Kriging method is an interpolation scheme that can be used for modeling deter-ministic computer analyses as the realization of a stochastic process. The technique has been recognized as an alternative to the traditional Response Surface method in generating approximation models of computationally expensive CFD analyses. This is due to its ability to interpolate sample data and to model a fu...

Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-d...