A new methodology has been developed to design optimal monitoring network to estimate the transient pollution plume resulting from active pollution sources in a contaminated aquifer. An optimization algorithm is linked with geostatistical kriging model as well as a numerical simulation model. Simulated annealing is used as the optimization tool. The physical process in the aquifer, i.e., the fl...

Kriging techniques are used to map national public health data routinely reported to the Centers for Disease Control and Prevention (CDC). Although choropleth maps are commonly used to display geographic and temporal disease-related events for political (state and county) jurisdictions, kriged maps may offer opportunities to enhance visual communication of event patterns over time. As a geostat...

Geostatistical models often require a variogram or covariance model for kriging and krigingbased simulation. Next to the initial decision of stationarity, the choice of an appropriate variogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental variograms with a nested combination of proven models such as the spherical, exponential, ...

Geostatistical simulations are used to perform a global sensitivity analysis on a model Y = f(X1 ... Xk) where one of the model inputs Xi is a continuous 2D-field. Geostatistics allow specifying uncertainty on Xi with a spatial covariance model and generating random realizations of Xi. These random realizations are used to propagate uncertainty through model f and estimate global sensitivity in...

We propose a novel approach to landmark-based medical image registration based on the geostatical method of Kriging prediction. Our method exploits the spatial statistical relation between two images, as estimated using generalpurpose registration algorithms, in order to construct an optimum predictor of the displacement field. High accuracy is achieved by using an estimated spatial model of th...

Contents Summary 4 1 Motivation: time series normalization and adjustment 5 1.