Reducing geological uncertainty by conditioning on boreholes: the coupled Markov chain approach

• Published in: Hydrogeology journal Vol. 15; no. 8; pp. 1439 - 1455
• Main Authors: Elfeki, Amro M. M, Dekking, F. M
• Format: Journal Article
• Language: English
• Published: Heidelberg Berlin/Heidelberg : Springer-Verlag 01.12.2007; Springer Nature B.V
• Subjects: Aquifers; Boreholes; Case studies; Conditioning; Coupled Markov chains; Geological engineering; Geological structures; geostatistics; Heterogeneity; Hydrology; Markov chains; Uncertainty quantification; Unconsolidated aquifers; ANE, Netherlands, Zuid-Holland, Rijn-Maas Delta; ANE, Netherlands
• ISSN: 1431-2174; 1435-0157
• DOI: 10.1007/s10040-007-0193-x

The CMC (coupled Markov chain) model, which is based on the extension of Markov chains in two-dimensions, is used in the reduction of uncertainty in geological structures when conditioned (i.e., honours the data and their location) on a number of boreholes. The model has been applied to an unconsolidated aquifer deposit located in the central Rhine-Meuse delta (the Gorkum study area) in the Netherlands. A comparison is also made between the CMC and the SIS (sequential indicator simulation) model, which is based on Kriging and co-Kriging theories on the same deposit. The results show the potential applicability of the CMC model in reducing the uncertainty in geological configurations when a sufficient number of boreholes is available. Reproduction of the global geological features requires relatively few boreholes (in this case study, nine boreholes with 30-m spacing over a distance of 240 m). However, reproduction of the proportion of each state requires a relatively large number of boreholes (in this case study 31 boreholes with 8-m spacing over a distance of 240 m). It has been shown that variograms can be deceptive in modeling the spatial pattern and that they reflect only part of the complete spatial structure in the field. The use of transition probabilities via the CMC model provides a better alternative approach, because it uses multiple point information.