Flores Orozco, A. and Kemna, A. and Binley, A. and Cassiani, G. (2019) Analysis of time-lapse data error in complex conductivity imaging to alleviate anthropogenic noise for site characterization. Geophysics, 84 (2). B181-B193. ISSN 0016-8033
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Abstract
Previous studies have demonstrated the potential benefits of the complex conductivity (CC) imaging over electrical resistivity tomography for an improved delineation of hydrocarbonimpacted sites and accompanying biogeochemical processes. However, time-lapse CC field applications are still rare, in particular for measurements performed near anthropogenic structures such as buried pipes or tanks, which are typically present at contaminated sites. To fill this gap, we have developed CC imaging (CCI) results for monitoring data collected in Trecate (northwest Italy), a site impacted by a crude oil spill. Initial imaging results reveal only a poor correlation with seasonal variations of the groundwater table at the site (approximately 6 m). However, it is not clear to which extend such results are affected by anthropogenic structures present at the site. To address this, we performed a detailed analysis of the misfit between direct and reciprocal time-lapse differences. Based on this analysis, we were able to discriminate spatial and temporal sources of systematic errors, with the latter commonly affecting measurements collected near anthropogenic structures. Following our approach, CC images reveal that temporal changes in the electrical properties correlate well with seasonal fluctuations in the groundwater level for areas free of contaminants, whereas contaminated areas exhibit a constant response over time characterized by a relatively high electrical conductivity and a negligible polarization effect. In accordance with a recent mechanistic model, such a response can be explained by the presence of immiscible fluids (oil and air) forming a continuous film through the micro and macropores, hindering the development of ion-selective membranes and membrane polarization. Our results demonstrate the applicability of CCI for an improved characterization of hydrocarboncontaminated areas, even in areas affected by cultural noise.