Pilot-scale field validation of the long electrode electrical resistivity tomography method

A validation experiment, carried out in a scaled field setting, was attempted for the long electrode electrical resistivity tomography method in order to demonstrate the performance of the technique in imaging a simple buried target. The experiment was an approximately 1/17 scale mock-up of a region encompassing a buried nuclear waste tank on the Hanford site. The target of focus was constructed by manually forming a simulated plume within the vadose zone using a tank waste simulant. The long electrode results were compared to results from conventional point electrodes on the surface and buried within the survey domain. Using a pole-pole array, both point and long electrode imaging techniques identified the lateral extents of the pre-formed plume with reasonable fidelity but the long electrode method was handicapped in reconstructing vertical boundaries. The pole-dipole and dipole-dipole arrays were also tested with the long electrode method and were shown to have the least favourable target properties, including the position of the reconstructed plume relative to the known plume and the intensity of false positive targets. The poor performance of the pole-dipole and dipole-dipole arrays was attributed to an inexhaustive and non-optimal coverage of data at key electrodes, as well as an increased noise for electrode combinations with high geometric factors. However, when comparing the model resolution matrix among the different acquisition strategies, the pole-dipole and dipole-dipole arrays using long electrodes were shown to have significantly higher average and maximum values within the matrix than any pole-pole array. The model resolution describes how well the inversion model resolves the subsurface. Given the model resolution performance of the pole-dipole and dipole-dipole arrays, it may be worth investing in tools to understand the optimum subset of randomly distributed electrode pairs to produce maximum performance from the inversion model.