PyKrige internally supports the six variogram models listed below. Additionally, the code supports user-defined variogram models via the 'custom' variogram model keyword argument. For stationary ...

Variograms are important tools in the spatial distribution of facies and petrophysical properties. Due to the scarcity of subsurface well data, both spatially and quantity wise, variograms ...

Functions: an ordner containing the following documents - Auxiliary-functions.R: auxiliary functions needed for the variogram estimations, e.g. to build the vectors for the MCD variogram estimators - ...

Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling. One part of ...

A Bayesian approach to covariance estimation and spatial prediction based on flexible variogram models is introduced. In particular, we consider black-box kriging models. These variogram models do not ...

In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order to ...

Abstract: Parameter estimation of variogram models is an important problem in geostatistics and environmental engineering. Most of existing works aim to estimate parameters of variogram single models, ...