The impact of random function model assumption for the identification of hydraulic conductivity fields in fractured media: Gaussian and non-Gaussian approaches

Abstract

[EN] In order to model the uncertainty about predictions based on groundwater flow and transport models, it is necessary to properly characterize the uncertainty of those parameters that vary spatially, such as porosity, water density or hydraulic conductivity. This paper presents an attempt to identify K in a 3D fractured medium within a non-Gaussian framework using the Conditional Probabilities Method (CPM), a non-Gaussian stochastic inverse modeling approach for the identification of K fields conditional to both direct K measurements and stationary pressure data.