Coupled hydrogeophysical methods infer hydrological and petrophysicalparameters directly from geophysical measurements. Widespread methods do notexplicitly recognize uncertainty in parameter estimates. Therefore, we applya sequential Bayesian framework that provides updates of state, parameters andtheir uncertainty whenever measurements become available. We have coupleda hydrological and an electrical resistivity tomography (ERT) forward code ina particle filtering framework. First, we analyze a synthetic data set oflysimeter infiltration monitored with ERT. In a second step, we apply theapproach to field data measured during an infiltration event on a full-scaledike model. For the synthetic data, the water content distribution and thehydraulic conductivity are accurately estimated after a few time steps. Forthe field data, hydraulic parameters are successfully estimated from watercontent measurements made with spatial time domain reflectometry and ERT, andthe development of their posterior distributions is shown.
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