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Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

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2010
EGU Katlenburg-Lindau

Hydrology and earth system sciences 14, 545 - 556 () [10.5194/hess-14-545-2010]

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Abstract: Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

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Note: We thank the reviewers for their constructive comments. J. Rings thanks the BAW Karlsruhe for the possibility to take measurements on the dike model and A. Scheuermann and A. Bieberstein at the IBF, University of Karlsruhe, for supporting the measurements. J. A. Huisman is supported by grant HU1312/21 of the Deutsche Forschungsgemeinschaft.

Research Program(s):
  1. Terrestrische Umwelt (P24)

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