Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geostatistics. While best prediction has been well understood univariate statistics, literature for case elusive so far. The new challenges provided by modern datasets, being typically multivariate, call a deeper study cokriging. In particular, we deal with problem misspecified cokriging wi...

The concentrations of As, Hg, Co, Cr and Cd were tested for each soil sample, and their spatial patterns were analyzed by the semivariogram approach of geostatistics and geographical information system technology. Multivariate statistic approaches (principal component analysis and cluster analysis) were used to identify heavy metal sources and their spatial pattern. Principal component analysis...

Since gstat package version 1.0-0, a dependency of gstat on the R package spacetime was introduced, allowing the code in gstat to exploit spatio-temporal data structures from that package. This vignette describes the possibilities and limitations of the package for spatio-temporal geostatistics. To understand some of the possibilities and limitations, some knowledge of the history of the softwa...

This paper presents an overview of the most recent developments in the field of geostatistics and describes their application to soil science. Geostatistics provides descriptive tools such as semivariograms to characterize the spatial pattern of continuous and categorical soil attributes. Ž . Various interpolation kriging techniques capitalize on the spatial correlation between observations to ...

Kriging is a widely employed technique, in particular for computer experiments, in machine learning or in geostatistics. An important challenge for Kriging is the computational burden when the data set is large. We focus on a class of methods aiming at decreasing this computational cost, consisting in aggregating Kriging predictors based on smaller data subsets. We prove that aggregations based...

Mapping data using geostatistics can be a time-consuming process because of the many parameters to define. automap, a geostatistical package written in R, was developed to define automatically a spatial correlation model, a step that is considered to be the biggest obstacle for automating the spatial interpolation process with geostatistical algorithms. The implementation of automap into a Serv...

Spatially distributed estimates of ecological variables are generally required for use in geographic information systems (GIS) and models when dealing with many environmental phenomena [2]. In particular, the quality of GIS products depends often on local properties that vary in space. In this context, increased interest has been demonstrated to geostatistics, or spatial statistics, as an analy...

The application of state-of-the-art history matching methods to large heterogeneous reservoirs is hampered by two main problems: (1) reservoir models should be constrained jointly to dynamic data and a large variety of geological continuity information, (2) CPU demand should not be prohibitive for large models. This paper proposes a method contributing to alleviating these two main concerns. By...