Identification of hydraulic conductivity via normal-score ensemble smoother with multiple data assimilation (NS-MDA) by assimilating hydraulic head or concentration

In this study, we compare the capability of the normal-score ensemble smoother with multiple data assimilation (NS-MDA) to identify hydraulic conductivity when it assimilates or hydraulic heads or concentrations. The study is performed in a two-dimensional numerical single point contamination experiment of an aquifer vertical cross section. Reference hydraulic conductivity maps are generated using geostatistics, and the groundwater flow and transport are solved to produce reference state variable data (hydraulic head and concentration). Assimilating data for the inverse problems are sampled in time at a limited number of points from the reference aquifer response. Prior variogram function of hydraulic conductivity is assumed and equally-likely realizations are generated. Stochastic inverse modelling is run using the NS-MDA for the identification of hydraulic conductivity by considering two scenarios: 1) assimilating hydraulic heads only and 2) assimilating concentrations only. Besides the qualitative analysis of the identified hydraulic conductivities maps, the results are quantified by using the average absolute bias (AAB) that represents a measure of accuracy between the reference values and the inversely identified values according each scenarios. The updated parameters reproduce the reference aquifer ones quite well for the two scenarios investigated, with better results for the scenario 1, indicating that NS-MDA is an effective approach to identifying hydraulic conductivities.