In this work a strategy for optimizing ERT data sets based on the sensitivity matrix is examined and compared with the existing optimization schemes as well as with the most commonly used traditional arrays. Synthetic data tests illustrate that existing ERT data optimization approaches are highly dependent on the subsurface resistivity. As a result we also propose optimizing ERT measurements for an average subsurface background resistivity model rather than homogeneous ground. This approach is not practical for routine data collection but is highly suited to time-lapse ERT monitoring. The effectiveness of the approach is demonstrated with synthetic examples run with an ERT inversion algorithm that is based on a finite element forward solver. Further, the presented algorithm is tested on field data. The results demonstrate that the optimum data sets can provide improved subsurface images in relation with existing arrays.


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