A BAYESIAN MARKOV GEOSTATISTICAL MODEL FOR ESTIMATION OF HYDROGEOLOGICAL PROPERTIES

A geostatistical methodology based on Markov-chain analysis and Bayesi an statistics was developed for probability estimations of hydrogeolog ical and geological properties in the siting process of a nuclear wast e repository. The probability estimates have practical use in decision -making on issues such as siting, investigation programs, and construc tion design. The methodology is nonparametric which makes it possible to handle information that does not exhibit standard statistical distr ibutions, as is often the case for classified information. Data do not need to meet the requirements on additivity and normality as with the geostatistical methods based on regionalized variable theory, e.g. kr iging. The methodology also has a formal way for incorporating profess ional judgments through the use of Bayesian statistics, which allows f or updating of prior estimates to posterior probabilities each time ne w information becomes available. A Bayesian Markov Geostatistical Mode l (BayMar) software was developed for implementation of the methodolog y in two and three dimensions. This paper gives (1) a theoretical desc ription of the Bayesian Markov Geostatistical Model; (2) a short descr iption of the BayMar software; and (3) an example of application of th e model for estimating the suitability for repository establishment wi th respect to the three parameters of lithology, hydraulic conductivit y, and rock quality designation index (RQD) at 400-500 meters below gr ound surface in an area around the Aspo Hard Rock Laboratory in southe astern Sweden.