Open AccessImproved strategies for the automatic selection of optimized sets of electrical resistivity tomography measurement configurations
Author(s) -
Wilkinson Paul B.,
Meldrum Philip I.,
Chambers Jonathan E.,
Kuras Oliver,
Ogilvy Richard D.
Publication year - 2006
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1111/j.1365-246x.2006.03196.x
Subject(s) - jacobian matrix and determinant , algorithm , image resolution , resistive touchscreen , tomography , inversion (geology) , electrical resistivity tomography , data set , matrix (chemical analysis) , resolution (logic) , inverse problem , computer science , independence (probability theory) , electrical resistivity and conductivity , mathematical optimization , mathematics , artificial intelligence , geology , mathematical analysis , optics , statistics , physics , computer vision , materials science , paleontology , structural basin , quantum mechanics , composite material
SUMMARY 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.