Determining Background Variations to Identify Significant Changes in Resistivity and IP for Time-lapse Experiments

Abstract

Electrical Resistivity Tomography conducted in time-lapse mode is frequently used for the monitoring of time-varying processes. Nevertheless, it is tricky to identify significant changes in resistivity and chargeability when monitored processes vary slowly in time. Moreover, the errors on acquired measurement might not be negligible as well as the uncertainties on the blocks of the reconstructed models. The uncertainty distribution depends especially on selected arrays, resistivity and chargeability distributions and model blocks sensitivities. An estimate of the background variations for every block of the models is thus required prior to the mapping of resistivity and IP changes during the monitoring experiment. A new methodology is proposed to estimate these background variations and to point out significant changes in resistivity and chargeability. This methodology is based on a two steps approach. The first step consists in determining a sensitivity cut-off value to estimate the depth of investigation. The second step aims to estimate resistivity and chargeability confidence intervals of each model block based on Monte-Carlo simulations. This methodology was applied to a field monitoring experiment conducted on a site contaminated with chlorinated solvents where biodegradation remediation is performed to assess its efficiency.