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A Bayesian Markov Geostatistical Model for Estimation of Hydrogeological Properties
Author(s) -
Rosen Lars,
Gustafson Gunnar
Publication year - 1996
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.1996.tb02081.x
Subject(s) - bayesian probability , computer science , kriging , data mining , hydrogeology , geostatistics , markov chain , statistics , geology , mathematics , machine learning , artificial intelligence , geotechnical engineering , spatial variability
A geostatistical methodology based on Markov‐chain analysis and Bayesian statistics was developed for probability estimations of hydrogeological and geological properties in the siting process of a nuclear waste repository. The probability estimates have practical use in decision‐making on issues such as siting, investigation programs, and construction design. The methodology is nonparametric which makes it possible to handle information that does not exhibit standard statistical distributions, 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. kriging. The methodology also has a formal way for incorporating professional judgments through the use of Bayesian statistics, which allows for updating of prior estimates to posterior probabilities each time new information becomes available. A Bayesian Markov Geostatistical Model (BayMar) software was developed for implementation of the methodology in two and three dimensions. This paper gives (1) a theoretical description of the Bayesian Markov Geostatistical Model; (2) a short description of the BayMar software; and (3) an example of application of the model for estimating the suitability for repository establishment with respect to the three parameters of lithology, hydraulic conductivity, and rock quality designation index (RQD) at 400–500 meters below ground surface in an area around the Äspö Hard Rock Laboratory in southeastern Sweden.