3D non-invasive monitoring of water flow and solute ...

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XVI International Conference on Computational Methods in Water Resources (CMWR-XVI) Ingeniørhuset

3D non-invasive monitoring of water flow and solute transport processes with MERIT
Author:Axel Tillmann <a.tillmann@fz-juelich.de> (Forschungszentrum Jülich GmbH)
Egon Zimmermann <e.zimmermann@fz-juelich.de> (Forschungszentrum Jülich GmbH)
Andreas Kemna <a.kemna@fz-juelich.de> (Forschungszentrum Jülich GmbH)
Harry Vereecken <h.vereecken@fz-juelich.de> (Forschungszentrum Jülich GmbH)
Presenter:Axel Tillmann <a.tillmann@fz-juelich.de> (Forschungszentrum Jülich GmbH)
Date: 2006-06-18     Track: Special Sessions     Session: Hydrogeophysical data fusion
DOI:10.4122/1.1000000467

The non-invasive measurement of physical parameters in soils is an important tool for observation and sustainable management of soils and aquifers so as to preserve or to restore groundwater quantity and quality from natural or anthropogeneous effects. Therefore, a spatially and temporally highly resolved monitoring and characterization of soils and aquifers is often required. A broad range of mechanistic models that predict water flow and solute transport in the subsurface environment is available now. However, to use and validate these models a proper set of data must be collected, preferrably without disturbing the soil or aquifer system. We present Magneto-Electrical Resistivity Imaging Technique (MERIT) to monitor fluid flow and solute transport processes at multiple scales by simultaneous measurement of electric potential and magnetic field data. We performed numerical modeling of the electric potential and magnetic field during a solute transport process in a cylindrical soil column. The obtained synthetic data sets of the electric potential and magnetic field were simultaneous processed by a 3D inversion algorithm of Gauss-Newton-type in order to determine the electrical conductivity distribution within the sample. The inversion algorithm itself is based on a parameterization of the cylindrical column as a set of functionals. Additional knowledge about the specific hydrological process can be introduced into the inversion scheme by chosing an appropriate set of functionals. The results of the inversion process depending of the chosen functional set vary in it's reproduction of the true model, convergence behaviour and computational speed.