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Stochastic Data Integration to model Quaternary Aquifers: Application on the Aare Valley, Switzerland
  • Alexis Neven,
  • Philippe Renard
Alexis Neven
University of Neuchâtel, Centre for Hydrogeology and Geothermics

Corresponding Author:[email protected]

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Philippe Renard
University of Neuchâtel
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Nowadays, the centralization of subsurface-related observation is more and more common. Databases constituted of an ensemble of geophysical, hydrological, or borehole measurement forms a unique source of information for subsurface hydro-geological modeling. However, the increasing amount of data and the importance of being able to update the model when new data becomes available creates a huge need for automatic workflow for such data integration, hopefully reeling on open-source technology. Quaternary deposits are complex to model due to the high heterogeneity they present. Over the last 2.5 Ma, multiple cycles of glacial and inter-glacial phases deposited complex intertwining of sedimentary pattern, with high contrast in parameters (e.g. : permeability, porosity, nature of sediment) and space. Nevertheless, these deposits are some of the most extensively used for water resources, shallow geothermal exploitation, or construction material exploitation. In this study, we propose a new method based on a flipped stochastic joint inversion, and applied it on complex Quaternary deposits. Geophysical, boreholes and hydrological observations are inverted together. Boreholes are used to generate stochastic geological models, populated with parameters. The inversion tune the geological model in order to fit the field data. Our method allows not only to integrate boreholes, geophysical and hydrological data, but also conceptual models, with a robust uncertainty estimation. The method was applied on some areas of the Upper Aare Valley (Switzerland), a valley covered by more than 58’000 geophysical measurement points, 1’500 boreholes and 100 of hydrological observations. Our method showed promising results in combining these data. Comparison with existing geological models proves that our automated method not only show realistic underground structures, but also significantly improved the regional knowledge of the underground by combining all the existing data, and will therefore lead to better decision making while being based on open-source technology.