We explore and compare different methods for the spatial clustering of geostatistical data. A new methodology based on the likelihood is proposed and compared to the approach by Allard and Monestiez (1999). Both methods are compared on a heavy metal concentration data set in the Swiss Jura.

Present address: Lafarge Canada, Corporate Technical Services, 6150 ave. Royalmount, Montréal, Québec, Canada H4P 2R3 Abstract This paper describes a geostatistical technique based on conditional simulations to assess confidence intervals of local estimates of lake pH values on the Canadian Shield. This geostatistical approach has been developed to deal with the estimation of phenomena with a s...

Raster based digital elevation models (DEM) are the basis of some of the most important GIS workflows: hydrologic modeling, site suitability, and cost path analysis. While there are several techniques for generating digital elevation models (DEMs), none of them can produce a true elevation surface. Locally varying measurement error and the inexactness of the interpolation methods contribute to ...

This paper focuses on an overview of kernel density estimation especially for what concerns the choice of bandwidth and intensity parameters according to local conditions. A case study inherent seismic risk analysis of the old town centre of Potenza hilltop town has been discussed, with particular attention to the evaluation of the possible local amplifying factors. This first integrated applic...

The Lower Shihezi Formation of the Daniudi gas field in Ordos Basin is a typical reservoir braided river system an alluvial plain, characterized by extensive development, parallel development from near source to center basin, and frequent interweaving cut stacking, as well complex deposition process that has seen channel changes. belt, channel, bar inside river, interlayer within constitute hie...

The first step in statistical data analysis is to verify three data features: dependency, stationarity, and distribution. If data are independent, it makes little sense to analyze them geostatisticaly. If data are not stationary, they need to be made so, usually by data detrending and data transformation. Geostatistics works best when input data are Gaussian. If not, data have to be made to be ...