10 research outputs found
Improved strategies for the automatic selection of optimized sets of Electrical Resistivity Tomography measurement configurations
Two strategies are presented for obtaining the maximum spatial resolution in electrical resistivity
tomography surveys using a limited number of four-electrode measurement configurations.
Both methods use a linearized estimate of the model resolution matrix to assess the effects of
including a given electrode configuration in the measurement set. The algorithms are described
in detail, and their execution times are analysed in terms of the number of cells in the inverse
model. One strategy directly compares the model resolution matrices to optimize the spatial
resolution. The other uses approximations based on the distribution and linear independence
of the Jacobian matrix elements. The first strategy produces results that are nearer to optimal,
however the second is several orders of magnitude faster. Significantly however, both offer
better optimization performance than a similar, previously published, method. Realistic examples
are used to compare the results of each algorithm. Synthetic data are generated for each
optimized set of electrodes using simple forward models containing resistive and/or conductive
prisms. By inverting the data, it is demonstrated that the linearized model resolution matrix
yields a good estimate of the actual resolution obtained in the inverted image. Furthermore,
comparison of the inversion results confirms that the spatial distribution of the estimated model
resolution is a reliable indicator of tomographic image quality