GNGTS 2017 - 36° Convegno Nazionale

GNGTS 2017 S essione 3.2 643 by the tank walls. In the second one, the reconstruction of the electric source current density required self-potential data along the cross-section perpendicular to the tank. Using the GCV approach (Wahba andWang, 1995) to selecting an appropriate regularization term and imposing weighted smoothness constraints on depth, the recovered current density distribution is seen to fit the observed data well, demonstrating that the inversion process is useful in quantitatively reconstruction of the causative source distribution in regions where an electronic conductor exists. References Arora T., Linde N., Revil A. and Castermant J. 2007. Non-intrusive determination of the redox potential of contaminant plumes by using the self-potential method. Journal of Contaminant Hydrology 92, 274–292. Castermant J., Mendonça C. A., Revil A., Trolard F., Bourrié G. and Linde N., 2008. Redox potential distribution inferred from self-potential measurements associated with the corrosion of a burden metallic body: Geophysical Prospecting, 56, 269–282. De Donno, G. and Cardarelli, E. 2014. 3D complex resistivity tomography on cylindrical models using EIDORS. Near Surface Geophysics 12(5), 587-598. Jacobsen M., Hansen P. C. and Saunders M. A., 2003. Subspace preconditioned LSQR for discrete ill-posed problems. BIT Numerical Mathematics, vol. 43, no. 5, pp. 975–989. Jardani A., Revil A., Barrash W., Crespy A., Rizzo E., Straface S., Cardiff M., Malama B., Miller C., Johnson T., 2009. Reconstruction of the water table from self-potential data. GroundWater 47, 213–227. Linde N. and Revil A., 2007. Inverting residual self-potential data for redox potentials of contaminant plumes. Geophysical Research Letters 34, L14302. Maineult A., Bernab´e Y. and Ackerer P., 2006. Detection of advected, reacting redox fronts from self-potential measurements. Journal of Contaminant Hydrology 86, 32–52. Mendonça C. A., 2008. Forward and inverse self-potential modeling in mineral exploration. Geophysics, 73, no. 1, F33–F43. Minsley B.J., Sogade J. and Morgan F.D. 2007. Three-dimensional self-potential inversion for subsurface DNAPL contaminant detection at the Savannah River Site, South Carolina. Water Resources Research 43, W04429. Revil A., Ehouarne L. and Thyreault E. 2001. Tomography of selfpotential anomalies of electrochemical nature. Geophysical Research Letters 28, 4363–4366. Revil A., Mendonça C. A., Atekwana E., Kulessa B., Hubbard S. S. and Bolhen K., 2010: Understanding biogeobatteries: Where geophysics meets microbiology. Journal of Geophysical Research, 115, G00G02. Rittgers J. B., Revil A., Karaoulis M., Mooney M. A., Slater L. D. and Atekwana E. A., 2013. Self-potential signals generated by the corrosion of buried metallic objects with application to contaminant plumes: Geophysics, 78, no. 5, EN65–EN82. Wahba G. and Wang Y., 1995. Behavior near zero of the distribution of the GCV smoothing parameter estimates. Statistics and Probability Letters 25, 105–111. Surface wave analysis for loose soil characterization M. Papadopoulou, L.V. Socco 1 , C. Comina 2 1 Politecnico di Torino, Italy 2 Università degli Studi di Torino, Italy Introduction. The characterization of loose sand formations in terms of seismic velocities and thickness is relevant in many engineering applications, ranging from seismic hazard identification and environmental studies to estimation of liquefaction potential and desertification phenomena. In oil and gas, the study of these materials is particularly interesting in static corrections, especially in sand dune environments. The use of surface wave analysis in these formations has been proven to be a very powerful tool that, in several cases, can overcome the limitations of other seismic methods. The dispersion curve (DC) can be experimentally retrieved by computing wavefield transforms of the raw seismic records and picking the energy maxima. Once the DC is estimated it can be inverted to provide a local 1D S-wave velocity model. For the inversion, the subsoil is modeled as a stack of homogeneous linear elastic layers which are characterized by four parameters: VS, VP, ρ (density) and H (thickness). The dispersion curve