Noniterative algorithms for electrical resistivity imaging applied to subsurface local anomalies

In this paper, we compare five noniterative (one-step) algorithms for two-dimensional electrical resistivity imaging applied to the location of subsurface local anomalies. Here, we analyze the performance of two backprojection algorithms and three algorithms based on a least-squares criterion. These five algorithms can also be adapted for process and medical tomography. Algorithm performance is first assessed from synthetic data derived from an analytical solution. We show that least-squares-based algorithms outperform backprojection algorithms in all situations considered. One of the least-squares algorithms was further validated with experimental measurements involving spherical objects immersed into a water tank. Data were obtained using a 16-electrode linear array and a computer-controlled data-acquisition system. A reference measurement before immersing the objects into the water tank reduced errors in the reconstructed image attributable to the uncertain electrode position and the finite dimensions of the tank. Images deteriorated for deeper objects, but neglecting measurements with the smallest signal-to-noise ratio improved the results.