This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas—contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampl...

Detailed maps of bed elevation and ice thickness are essential for understanding and projecting the evolution of the ice sheets. Such maps are traditionally obtained using airborne radarsounding profiler data interpolated onto regular grids using geostatistical tools such as kriging. Here we compare three mapping techniques applied to a dense radar survey of Russell Glacier, West Greenland, by ...

This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then...

In order to prevent landslide and collapse in the mining area, it is essential not only gain a full understanding of surface deformation information about open-pit area surrounding dumps, but also closely monitor development deformation. Small baseline set (SBAS) technology can be applied extract high-resolution spatial-temporal from areas. Due decoherence, however, difficult obtain effective o...

Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-valida...

The objective of this work is to extend kriging, a geostatistical interpolation method, to honor parameter nonnegativity. The new method uses a prior probability distribution based on reflected Brownian motion that enforces this constraint. The work presented in this paper focuses on interpolation problems where the unknown is a function of a single variable (e.g. time), and is developed both f...

When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly dif...

The empirical semivariogram of residuals from a regression model with stationary errors may be used to estimate the covariance structure of the underlying process. For prediction (kriging) the bias of the semivariogram estimate induced by using residuals instead of errors has only a minor eeect because the bias is small for small lags. However, for estimating the variance of estimated regressio...

In adaptive sampling and optimization methods, the uncertainty estimators are used to guide the selection of the next sampling point(s). These algorithms often limit themselves to surrogates such as kriging and polynomial response surface because of the lack of uncertainty estimates in the implementation of other surrogates. We propose the importation (borrowing) of uncertainty estimates from o...

The accuracy of the Digital Elevation Models (DEMs) interpolated using four different methods was compared in two small watersheds in the Ecuadorian Andes. The interpolation methods compared were Inverse Distance Weighted, radial basis functions, ordinary Kriging, and TOPOGRID. A qualitative comparison was made between the stream network modeled in Arc Hydro and the digitized stream network. Mo...