Optimierte Informationsverarbeitung in Methoden zur stochastischen Simulation und zur Abschätzung von Parameterwerten: Unsichere zeitabhängige Strömungs- und Transportvorgänge im Untergrund

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Subsurface water quantity and quality is usually predicted using numerical models. Their parameters have unknown spatial distributions which can be estimated from site data using geostatistical inversion techniques. Site data often include time series from well tests or tracer experiments which convey redundant information due to their temporal auto-correlation, leading to excessive computational costs in parameter estimation. Much quicker calculations are possible using temporal moments of time series instead, which is an efficient method to summarize information. Unfortunately, temporal moments still do not constitute independent information, leading to accuracy problems and still allowing further improvement. In this project, temporal moments will be replaced by generalized functionals of time series, including ratios of moments. This is expected to result in a set of independent information units, yielding a basis for rapid and accurate information processing in parameter estimation and stochastic simulation of largescale time-dependent systems. For the first time, concepts from optimal design will be used to optimize information processing and to identify optimal sets of functionals. Also, a method to reconstruct time series from these functionals will be developed. Measured data from a real site will be used to test the developed methods, with applications to parameter estimation and stochastic modeling.