A reverse engineering approach to optimal design of site investigation schemes and monitoring networks

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The goal of experimental design is to investigate a system such that maximum information is achieved from a given, limited budget for investigation. Looking at groundwater as a drinking water resource, its quality and safety is hard to assess and predict mainly because subsurface materials are heterogeneous, and there are too few data to resolve this heterogeneity and extract the necessary key properties of the system. Experimental design can help to optimize subsurface exploration, maximizing the desired prediction confidence of groundwater quality and safety in spite of limited exploration budgets. However, experimental design is currently heavily limited by its computer demands, especially in complex large-scale problems such as stochastic groundwater quality models. The current project will re-formulate optimal design theory within a new information-theoretic perspective. Instead of traditionally and tediously searching through all possible measurement locations to find the most informative ones, the direction of analysis will be reverted to directly identify the most informative ones in a single step. This will dramatically reduce the computational load, allowing to apply optimal design for larger, more complex and hence more realistic problems. At the same time, this avoids simplifications or linearizations that compromise the information content of data sets in the analysis. Instead of giving in to computational restrictions, the new method will allow tackling current, goal-oriented and application- driven research tasks. At first developed and applied within the groundwater perspective, the new method will then be exported as a valuable tool for many scientific disciplines that face similar uncertainties in heterogeneous or complex dynamic systems.