Covariance and variogram functions have been extensively studied in Euclidean space. In this article, we investigate the validity of commonly used covariance and variogram functions on the sphere. In particular, we show that the spherical and exponential models, as well as power variograms with 0 < α ≤ 1, are valid on the sphere. However, two Radon transforms of the exponential model, Cauchy mo...

Variograms and covariance functions are key tools in geostatistics. However, various properties, characterizations, and decomposition theorems have been established for covariance functions only. We present analogous results for variograms and explore the connections to covariance functions. Our ndings include criteria for covariance functions on intervals, and we apply them to exponential mode...

To study the applicability of geostatistics for vector data, wind velocity data have been analyzed statistically and geostatistically. The study area consists of two districts, the mountainous Chubu and plain Kanto districts in central Japan. For the distribution of wind speeds, exponential distribution was fitted well in both districts. Temporal experimental variograms of wind speeds, directio...

In this paper catchments are conceptualised as linear space-time filters. Catchment area A is interpreted as the spatial support and the catchment response time T is interpreted as the temporal support of the runoff measurements. These two supports are related by T∼A which embodies the space-time connections of the rainfall-runoff process from a geostatistical perspective. To test the framework...

A hierarchical geostatistical analysis is conducted on a high-resolution, multiscale hydraulic conductivity (lnK) map, created by scaling up an experimental stratigraphy. Unlike a previous study which evaluates lnK variograms within individual depositional environments, this study analyzes deposits (or samples) that incorporate multiple depositional environments. Based on conductivity cutoffs s...

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, ...

a field experiment was conducted to investigate the spatial distribution of weeds at the sari agricultural sciences and natural resources university in 2011. field was divided into 42 grids (1.5×1.5 feet) then all samples were taken from grids intersection points before corn planting, after harvesting and ear emergence stage. weed maps and semi variance analysis carried out using rw99 and gs+ s...

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...