A spatial analysis of variance uses the spatial dependence among the observations to modify the usual interference procedures associated with a statistical linear model. When spatial correlation is present, the usual tests for presence of treatment effects may no longer be valid, and erroneous conclusions may result from assuming that the usual F ratios are F distributed. This is demonstrated u...

Uniaxial compressive strength (UCS) is one of the most significant factors on the stability of underground excavation projects. Most of the time, this factor can be obtained by exploratory boreholes evaluation. Due to the large distance between exploratory boreholes in the majority of geotechnical projects, the application of geostatistical methods has increased as an estimator of rock mass pro...

Traditionally, geostatistical algorithms are contained within specialist GIS and spatial statistics software. Such packages are often expensive, with relatively complex user interfaces and steep learning curves, and cannot be easily integrated into more complex process chains. In contrast, Service Oriented Architectures (SOAs) promote interoperability and loose coupling within distributed syste...

[1] This study presents monthly CO2 fluxes from 1997 to 2001 at a 3.75 latitude 5 longitude resolution, inferred using a geostatistical inverse modeling approach. The approach focuses on quantifying the information content of measurements from the NOAA-ESRL cooperative air sampling network with regard to the global CO2 budget at different spatial and temporal scales. The geostatistical approach...

Genome-wide selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. The success of GS approaches will depend crucially on the availability of efficient and easy-to-use computational tools. Therefore, approaches that can be implemented using mixed models hold particular promise and deserve detailed study. A particular class of mixed models suitable ...

This chapter proposes a clear methodology on how to use machine learning algorithms for spatial data analysis in order to avoid any bias and eventually obtain fair estimation of their performance on new data. Four different machine learning algorithms are presented, namely multilayer perceptrons (MLP), mixture of experts (ME), support vector regression (SVR) and a local version of the latter (l...

Stochastic seismic inversion is an important part of geostatistical method, and the combination sequence simulation. In process random inversion, firstly, histogram distribution variables counted, variation function calculated, range in different directions determined. Then, for each implementation, traces are synthesized at channel position according to calculated reflection coefficient, error...

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. No...

The performance of geostatistical and spatial interpolation techniques for estimation of spatial variability of heavy metals and water quality mapping of groundwater resources in Ramiyan district (Golestan province- Iran) were investigated. 24 spring/well water samples were collected and the concentration of heavy metals (Ni, Co, Pb, Cd and Cu) was determined using Differential Pulse Polarograp...