Department of Statistics Seminar
North Carolina State University

presents

Dr. Bani Mallick

Texas A & M University

"Analyzing non-stationary spatial data using piecewise Gaussian processes"

ABSTRACT

Spatial processes are important modeling tools for Geostatistical problems. Classical geostatistics is based on the assumption of stationarity and rely on a variogram model to account for the spatial dependence in the observed data. It is widely recognized that in several occasions the assumption of constant spatial structure throughout the sampling region is violated. In this paper a new class of nonstationary processes is proposed based on piecewise stationary processes which is ideally suitable to model true sharp changes in spatial structures. In particular we have used piecewise Gaussian processes due to its nice interpretation and direct link with piecewise kriging but the methodology can be extended for any piecewise stationary processes. Voronoi tessellation is used to partition the non-stationary random field into locally stationary isotropic random fields. Bayesian hierarchical model is used to incorporate uncertainty in the specification of the nonstationarity. Reversible jump MCMC (Markov Chain Monte Carlo) is exploited to obtain the number and location of the partitions. This method is applied to permeability data of the La Cira field in the Magdalena middle basin, Colombia, South America.

Friday, September, 27, 2002

3:35 - 4:35 pm

206 Cox Hall

Refreshments will be served on the second floor of Dabney Hall (left of Room 222) at 3:00 pm.