Reconstruction of the water table from self-potential data: a bayesian approach
A Jardani1, A Revil, W Barrash
1Colorado School of Mines, Department of Geophysics, Golden, CO 80401, USA.
Ground Water
|November 20, 2008
Summary
Self-potential (SP) signals reveal groundwater flow patterns, especially when combined with potentiometric data. This study uses Bayesian inversion to map water table shapes in heterogeneous aquifers, improving subsurface flow estimation.
Area of Science:
- Geophysics
- Hydrogeology
- Environmental Science
Background:
- Groundwater flow generates self-potential (SP) signals detectable at the surface.
- Estimating subsurface flow patterns is crucial for water resource management.
- Aquifer heterogeneity complicates traditional flow modeling.
Purpose of the Study:
- To develop and validate an inversion method for self-potential signals.
- To account for aquifer heterogeneity and electrical resistivity-streaming current coupling.
- To improve the reconstruction of water table geometry using SP data.
Main Methods:
- Bayesian inversion framework for self-potential data.
- Integration of SP signals with potentiometric level measurements.
- Application to field data from hydraulic tomography and electrical resistivity tomography experiments.
Main Results:
- Synthetic tests demonstrate the advantage of combining SP and potentiometric data for water table reconstruction.
- Field application at the Boise Hydrogeophysical Research Site shows SP sensitivity to potentiometric variations.
- Nonlinearity due to high pumping rates (Reynolds number effects) identified as a challenge for current SP tomography.
Conclusions:
- The proposed Bayesian inversion method enhances groundwater flow characterization.
- Self-potential tomography is a valuable tool for mapping water table dynamics.
- Further research is needed to incorporate nonlinear flow regimes into SP tomography models.
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