2023
DOI: 10.5194/hess-2023-15
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Data-driven estimates for the geostatistical characterization of subsurface hydraulic properties

Abstract: Abstract. The geostatistical characterization of the subsurface is confronted with the double challenge of large uncertainties and high exploration costs. Making use of all available data sources is consequently very important. Bayesian inference is able to mitigate uncertainties in such a data scarce context by drawing on available background information in form of a prior distribution. To make such a prior distribution transparent and objective, it should be calibrated against a data set containing estimates… Show more

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