Geostatistical inference of hydraulic conductivity and dispersivities from hydraulic heads and tracer data

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Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator (2009, background): “Simultaneous estimation of a space-dependent dispersivity also helps to overcome the lack of dispersion [Nowak and Cirpka, 2006], but entails a scale dependence on the support volume of available tracer data.”
Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain (2010, methodology): “Once the design is decided upon and the data become available, we condition the log conductivity field by the Kalman Ensemble Generator (KEG) [Nowak, 2009b], which is an adaptation of the EnKF to geostatistical inversion.”
Large‐scale hydraulic tomography and joint inversion of head and tracer data using the Principal Component Geostatistical Approach (PCGA) (2014, methodology): “A typical example of this type is the joint inversion of hydraulic head and tracer test data [Harvey and Gorelick, 1995; Cirpka and Kitanidis, 2000; Nowak and Cirpka, 2006], which uses coupled flow and transport simulations to incorporate hydraulic head and tracer concentration measurements to…”