GNGTS 2016 - Atti del 35° Convegno Nazionale

610 GNGTS 2016 S essione 3.3 Bibliografia Cooper, G. R. J. and D. R. Cowan, 2009, Terracing potential field data: Geophysical Prospecting, 57, 1067–1071, doi: 0.1111/j.1365-2478.2009.00791.x. Cordell, L. and A. E. McCafferty, 1989, A terracing operator for physical property mapping with potential field data: Geophysics, 54, 621–634, doi:10.1190/1.1442689. Forgy, E.W., 1965, Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics, 21, 768–769. Li, X., 2016, Terracing gravity and magnetic data using edge-preserving smoothing filters: Geophysics, 81, G41–G47, doi: 10.1190/GEO2015-0409.1. Sun, J. and Y. Li, 2015, Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzy c-means clustering. Geophysics, 80, ID1-ID18. doi: 10.1190/geo2014-0049.1. Williams, S., Fairhead, J.D. and G. Flanagan, 2002, Realistic models of basement topography for depth to magnetic basement testing, SEG Expanded Abstracts, 21, 814–817, doi: 10.1190/1.1817384. 2D APPROACH FOR SOLVING SELF-POTENTIAL PROBLEMS: MODELING AND NUMERICAL SIMULATIONS I. Oliveti, E. Cardarelli DICEA, Area di Geofisica, Sapienza Università di Roma, Italy Introduction. The self-potential (SP) method is a well-established geophysical technique that has been applied, since its inception in the early 19 th century, to mineral exploration, oil well logging, geothermal exploration and more recently hydrogeologic, environmental and engineering investigations. In the past, this geophysical technique has been considered as a qualitative method, but nowadays, different quantitative interpretations have been proposed to define the geometry and density of the causative source. Sill (1983) was the first to introduce a physics-based approach to simulate numerically the self-potential using the finite difference method. Later, self-potential signals have been modeled by implementing the finite element approach (Soueid Ahmed et al. , 2013) and the finite volume method (Sheffer and Oldenburg, 2007). The two main contributions to the SP signals are the streaming potential associated with the drag of the excess of charge by the flow of the pore water and the “electro-redox” effect associated with redox potential gradients. Most recent applications of the SP method include investigations aimed at reconstructing the hydraulic head variations caused by pumping tests (Titov et al. , 2015) and detecting leakage paths in earth dams. The SP data have been also used to locate subsurface cavities (Jardani et al. , 2006) and the preferential flow pathways in geothermal field and active volcanoes. Several other recent efforts have focused on applications in mapping and assessment of contaminant plumes (Rittgers et al. , 2013). In this work, we introduce a two dimensional numerical modelling tool in MATLAB for predicting the SP response. Self-potential signals are obtained by starting with the solution of the groundwater flow or the electro-redox problem, then computing the source current density, and finally calculating the electrical potential. Selected case studies are presented in order to simulate both the electric field resulting from the existence of a leak in the dam and SP signals associated with a pumping test in an unconfined aquifer. In addition, to illustrate the efficacy of the algorithm, field data are examined. Theoretical background. A MATLAB code based on the finite element method is implemented to provide numerical 2D modelling of SP. The approach described here is based on the well-known constitutive equation: ∇⋅ ( σ ∇ ψ ) = J where σ (in Sm -1 ) and ψ (in V) are the electrical conductivity and the electrical potential,