Deep Aquifer Characterization with Magnetotellurics, Self-potential, and Hydrogeological Data Sets

Abstract:

Estimating hydraulic conductivity in heterogeneous aquifers with only hydrogeological data is challenging due to the limited number of well locations compared to the size of the model. Geophysics surveys, such as Electric Resistivity Tomography (ERT) and Magnetotellurics (MT) at the ground surface and/or in boreholes, can provide additional information on the subsurface hydrogeological structure, as well as help interpolate data between the wells. However, the need arises to identify a suitable petrophysical relationship between hydraulic conductivity and electric conductivity, which may not be uniquely determined. In this presentation, we propose a joint inversion method that does not assume any petrophysical relationship, by incorporating MT, self-potential (SP) and hydrogeological data sets. In the proposed framework, hydraulic conductivity and electrical conductivity fields are simultaneously estimated through self-potential data fitting that links the groundwater velocity to the electrical conductivity. The self-potential forward problem is solved with a spectral method that allows for a flexible calculation of the derivatives of velocity fields as required in the governing equation. Principal Component Geostatistical Approach (PCGA) is implemented to estimate the high-dimensional hydraulic conductivity and electric conductivity fields in synthetic heterogeneous aquifers and to quantify its estimation uncertainty utilizing a few hundreds of forward model runs.