GNGTS 2017 - 36° Convegno Nazionale

GNGTS 2017 S essione 3.2 639 Cassiani, G., Boaga, J., Vanella, D., Perri, M. T., & Consoli, S. (2015). Monitoring and modelling of soil–plant interactions: the joint use of ERT, sap flow and eddy covariance data to characterize the volume of an orange tree root zone. Hydrology and Earth System Sciences ,  19 (5), 2213-2225. Cassiani, G., Boaga, J., Vanella, D., Perri, M. T., & Consoli, S. (2014). Monitoring and modelling of soil–����� plant interactions: the joint use of ERT, sap flow and Eddy Covariance data to characterize the volume of an orange tree root zone. Hydrology and Earth System Sciences Discussions ,  11 , 13353-13384. Mary, B., Peruzzo, L., Boaga, J., Schmutz, M., Wu, Y., Hubbard, S. S., & Cassiani, G. (2017). On the reliability of Mise- à -la-Masse measurements for small-scale characterization of vine plant root zone: a case study in a Bordeaux Vineyard (France). �� ����� In prep. Schlumberger, C. (1920). Etude sur la prospection electrique du sous-sol . ����������������� Gauthier-Villars. 2D INVERSION OF ELECTRO-REDOX POTENTIAL SIGNALS GENERATED BY A METALLIC CONDUCTOR I. Oliveti, E. Cardarelli DICEA, Area di Geofisica, Sapienza Università di Roma, Italy Introduction. The occurrence of high amplitude self-potential (SP) anomalies associated with the presence of ore deposits and connected to the distribution of the redox potential of the ground water has been known since the nineteenth century. Recent efforts in this field have attempted to provide a quantitative interpretation of these anomalies by performing inverse modeling (Revil et al ., 2010; Mendonça, 2008). The inversion of self-potential data in terms of the distribution of the redox potentials has been carried out in ore prospection and in environmental applications where the self-potential method has been considered as a non-intrusive sensor of the distribution of the redox potential over contaminant plumes after removal of the streaming potential component (Maineult et al., 2006; Arora et al., 2007). It has been also used to locate metallic objects in sandbox experiments (Castermant et al ., 2008; Rittgers, 2013) and in the ground and abandoned boreholes because of the corrosion of their metallic casing. Different inversion algorithms have recently been developed to localize the causative source of electrical signals. Minsley et al. (2007) proposed an algorithm to invert self-potential data in terms of the distribution of the volumetric current (the divergence of the source current density) at depth by solving Poisson’s equation for the self-potential. Revil et al. (2001) introduced a cross-correlation algorithm to localize the intersection of ore bodies with the water table. Linde and Revil (2007) solved the Poisson equation to determine the distribution of the redox potential at depth over the contaminant plume associated with the presence of a municipal landfill. In this work, we present a sandbox experiment to evaluate the relationship between self- potential signals and redox potentials and to investigate the effectiveness of the inversion of experimental self-potential data to recover the source current density vector field using the LSQR method least-squares 2D finite-element modeling approach. Inverse modeling. The inversion of the self-potential data measured at the ground surface consists in determining the spatial distribution of the amplitude and direction of the source current density vector responsible for the observed (sampled) self-potential anomalies. The self-potential in V at a set of electrodes caused by the density sources in Am −2 at a set of points can be written as (Jardani et al ., 2009): (1) where K ( P , M ) denotes the Kernel connecting the SP data measured at point P , σ in Sm −1 the electrical conductivity and E H in V the redox potential.