Tackling capacitive coupling in broad-band spectral electrical impedance tomography (sEIT) measurements by selecting electrode configurations

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SUMMARY Electromagnetic (EM) coupling effects including both inductive and capacitive coupling have long been an essential problem in broad-band spectral electrical impedance tomography (sEIT) measurements at the field scale. Efforts have been made to remove EM coupling numerically or to suppress the effects by modified data acquisition strategies. For near-surface applications with relatively small survey layouts, inductive coupling can be well removed in the mHz to kHz frequency range. With the use of shielded coaxial cables and so-called active electrodes where the amplifiers are mounted at the electrodes, capacitive coupling in sEIT measurements can also be reduced. However, it remains challenging to cope with capacitive coupling between the cable shield and the ground, especially in resistive field conditions. The aim of this study is to deal with this type of capacitive coupling effect by identifying and filtering out sEIT measurements that are strongly affected by capacitive coupling. Based on a correction method for capacitive coupling proposed in a previous study, an approach to estimate measurement errors due to capacitive coupling is presented first. In the second step, a workflow was proposed to calculate the capacitive coupling strength (CCS) for each electrode configuration, which is defined as the ratio of the imaginary part of the impedance induced by capacitive coupling and the imaginary part of the impedance due to the subsurface electrical conductivity. In the final step, measurements with low CCS were selected for inversion and the results were compared with inversion results obtained using the previously developed correction approach. It was found that the filtering method based on CCS is more capable in tackling capacitive coupling compared to using model-based corrections. Spectrally consistent sEIT results up to kHz were obtained using the newly developed filtering method, which were not achieved in previous work using model-based correction.