Earth science studies deal in general with multivariate, regionalized, observations, which may be compositional, or not. Frequently, it is of interest to know whether those data have to be divided into different populations, a task usually performed by cluster analysis. This problem cannot be studied with traditional methods because samples are not independent. In that case, an extension of War...

In this article objective have been made to reviews different geostatistical methods available to estimate and simulate petrophysical properties (porosity and permeability) of the reservoir. Different geostatistical techniques that allow the combination of hard and soft data are taken into account and one refers the main reason to use the geostatistical simulation rather than estimation. Uncert...

In this study, a framework for network-based representation of time series is presented. the proposed method, initially, segmentation procedure completed by dividing signals in domain into fixed-width windows with 50% overlap. Each segment normalized based on range defined absolute maximum amplitude value main signal and its negative counterpart, are quantized to 2^n levels. This transformation...

abstract soil invertebrate and their spatial pattern are affected strongly by environmental factors. spatial variability of soil properties is one of the most important reasons of the macrofauna variability. this study was conducted to investigate the spatial variability of soil properties and soil macrofauna biomass in the riparian forest landscape of karkhe river. soil macro fauna were sample...

The optimal choice of sites to make spatial prediction is critical for a better understanding of really spatio-temporal data. It is important to obtain the essential spatio-temporal variability of the process in determining optimal design, because these data tend to exhibit both spatial and temporal variability. Two new methods of prediction for spatially correlated functional data are consider...

This paper reports on research into interactive, dynamic statistical graphics (DSG) applied to spatial data maintained within a geographic information system (GIS). We developed a bidirectional link between ArcView 2.1TM, a GIS, and XGobi, a DSG program. Multivariate data, collected at geographic locations, is passed from ArcView to XGobi and can be dynamically analyzed. The relation between th...

Model variograms describe the space domain statistics of magnetic and gravity data. Variogram analysis can be used to map intensity, depth, and scaling exponent (self-correlation) of source. In previous statistical methods the measured data were gridded and transformed to the wavenumber domain; then their power spectrum was analyzed using a spectral model. To avoid the loss and distortion of in...

A geostatistical perspective on spatial genetic structure may explain methodological issues of quantifying spatial genetic structure and suggest new approaches to address them. We use a variogram approach to (i) derive a spatial partitioning of molecular variance, gene diversity, and genotypic diversity for microsatellite data under the infinite allele model (IAM) and the stepwise mutation mode...