As the development of earth sciences and cross-disciplinary, it’s more and more meaningful and valuable to analyze and estimate precipitation spatial distribution. Precipitation spatial information is important in many fields, such as water resource management, drought and flood disaster predication, and regional sustainable development. It’s unrealistic to get the accurate predication of a par...

When analyzing data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis of Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensiv...

soil temperature is one of the most effective parameters for plant growth. although the spatiotemporal monitoring of soil temperature is important for providing an optimum condition to maximize the yield efficiency, indirect measurements for zoning  of soil temperature are essential due to difficulties in direct methods of measuring it and also the limited measurement points. thus, the efficien...

Synopsis The regression effect in ore-reserve estimation has been identified, described and quantified in vein and reef deposits for more than twenty years. Its importance in ‘three-dimensional’ deposits whether underground or open-pit -has been recognized under the alternate guise of the geostatistical volume-variance relationship. The Fontainebleau school has developed the technique of disjun...

the present study was carried out to evaluate three interpolation methods including weighted moving average (wma) with the power of 2 and 3, kriging and cokriging methods. data of 23 wells in mazandaran province were collected in fall and spring 2006. seven parameters including electrical conductivity (ec), ph, total dissolved solids (tds), sodium adsorption ratio (sar), total hardness (th), ch...

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters, and then to predict the phenomenon under study at unobserved sites within the corresponding sampling domain. The method of universal kriging for spatial prediction was introduced to cover the problem of spa...