Biography Dr. Dan Cornford is a lecture in Computer Science and works in the Neural Computing Research Group at Aston University. Research interests are in the field of spatial statistics, space-time modelling and data assimilation. Lehel Csato is a post-doc in the same group working on an EPSRC grant (GR/R61857/01) looking at applying sparse sequential Gaussian processes to data assimilation. ...

Geostatistics has become a preferred tool for the identification of lithofacies from sparse data, such as measurements of hydraulic conductivity and porosity. Recently we demonstrated that the support vector machine (SVM), a tool from machine learning, can be readily adapted for this task, and offers significant advantages. On the conceptual side, the SVM avoids the use of untestable assumption...

Temporal and Spatial Modeling of Groundwater Level in Bushehr Plain using Artificial Intelligence Geostatistics

Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S…x) based on observations Yi ˆ S…xi † ‡ Zi at sampling locations xi , where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly...

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understan...

Thin irregularly-shaped surfaces such as clay drapes often have a major control on flow and transport in heterogeneous porous media. Clay drapes are often complex curvilinear 3dimensional surfaces and display a very complex spatial distribution. Variogram-based stochastic approaches are often also not able to describe the spatial distribution of clay drapes since complex, curvilinear, continuou...

Differences in spatial supports (shape, size, etc.) among spatial data often complicate spatial data analyses, and spatial support conversions, such as aggregation, disaggregation, and interpolation, are often applied to cope with them. However, in general, spatial support conversions present the following problems: (i) how to convert data accurately? and (ii) how to reduce biases caused by the...