Random Fields for Spatial Data Modeling

A Primer for Scientists and Engineers

This book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the development of spatial models and their applications in data analysis.

DOI: 10.1007/978-94-024-1918-4.   Hardcover ISBN: 978-94-024-1916-0

Heal-LINK users can download an electronic version of the book by logging on their institutional HEAL-LINK account. 



The Geostatistics Laboratory was founded in 2002, and it is headed by Prof. Dionissios Hristopulos  (PhD).  (A personal but not quite up-to-date web-site is found at this link.)

The GSLAB lab members have expertise in the following areas: 

  1. Development of novel Geostatistical methods
  2. Fast algorithms for reconstruction of missing data
  3. Interpolation and simulation of scattered spatial and spatio-temporal data
  4. Numerical and theoretical models for random media
  5. Stochastic models of transport and other physical processes
  6. Statistical models of interevent times between extreme events such as earthquakes
  7. Applications of statistical physics to environmental problems

Some typical applications of interest include the following:

  1. Quantitative analysis of groundwater level variability (e.g., Messara valley of Crete)
  2. Estimates of mineral resources reserves (e.g., lignite fields of Western Greece)
  3. Statistical models of seismic risk assessment
  4. Analysis of GPS time series
  5. Environmental risk assessment based on geostatistical analysis
  6. Geostatistical analysis of precipitation
  7. Statistical models of mechanical strength, fracture, and earthquake recurrence times
  8. Novel space-time models based on statistical field theories
  9. Nonlinear kinetic models of grain growth and decay
  10. Principal mode analysis of damped harmonic oscillator in heat bath