Reconciling Electrical Resistivity Tomography Images and Hydrological Models
Abstract
Electrical resistivity tomography (ERT) holds the promise of providing information on the parameters controlling flow and potentially monitoring flow itself. The resolution of electrical resistivity tomography surveys is limited by the data quantity, data quality, and the survey configuration (electrode count, spacing, and borehole separation). Inevitably, we must interpret data from regions containing substantial variability at a scale much finer than the resolution of the method. This study used earlier field experiments performed at the Sandia-Tech Vadose Zone facility near Socorro, NM as a template to study the effects of fine-scale structure on geophysical images. A series of hydrological models were created that ranged from homogeneous half-space models to models with layers of random but spatially correlated hydrological parameters. A 12 x 12 x 12 m was modeled using grid sizes as small as 6.25 cm. Moisture content was converted to electrical conductivity using Archie's equation and forward modeled using a mesh at the same scale as the hydrological models. Normally-distributed, pseudo-random noise was added to create synthetic data. These data were then inverted with the same coarse mesh used to interpret field results from the original project. Modeling of ERT data for very large meshes was somewhat more difficult than anticipated and required determining optimal choices for mesh size particularly in the background and boundary regions. Initial results showed that ERT produces images that are smoother than the correct model, but preserves large-scale features. In fact, strongly layered, non-stochastic models appeared to be more difficult to image than the complex stochastic ones. However, the nature of the images of synthetic model data and the original field data were far more different than anticipated. The models had higher infiltration rates, higher moisture contents, sharper boundaries around the infiltration region, and larger contrasts between the pre- and post-infiltration saturations. The initial inversion parameters had to be adjusted to accommodate the larger contrasts present in the synthetic models. This also brought into question whether the hydrological models were representative of realistic field conditions. Work is underway to revise both the pre-infiltration and post-infiltration hydrological models to more closely approximate field conditions.
- Publication:
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AGU Spring Meeting Abstracts
- Pub Date:
- May 2005
- Bibcode:
- 2005AGUSMNS12A..03L
- Keywords:
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- 3210 Modeling;
- 3260 Inverse theory