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http://hdl.handle.net/2122/4876
Authors: | Meo, M.* Tammaro, U.* Capuano, P.* |
Title: | Influence of topography on ground deformation at Mt.Vesuvius (Italy) by finite element modelling | Journal: | International Journal of non linear mechanics | Series/Report no.: | /43 (2008) | Publisher: | Elsevier | Issue Date: | Apr-2008 | DOI: | 10.1016/j.ijnonlinmec.2007.12.005 | Keywords: | Ground deformations Geodetic monitoring Topography |
Subject Classification: | 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring | Abstract: | Ground deformations are observed in connection with volcanic activity, and therefore, geodetic monitoring can provide significant indication of changes of equilibrium conditions. The aim of this paper is to study the deformation of Mount Vesuvius (Italy) caused by overpressure sources at various depths, using a commercial (Ansys) 3D finite element code, in the framework of linear elastic isotropic material behavior. Both homogenous and heterogeneous media with carbonate basement were analyzed to understand the influence of topography on the ground deformations. The topography of the Somma-Vesuvius was taken into account, using a digital terrain model, and the carbonate basement was schematically modelled by assuming two horizontal layers with different Young moduli. The presence of a strong deviation from axially symmetric pattern of the displacement field, and of small subsidence areas, was found. These characteristics are completely unknown from the simple Mogi model and by simplified topography model, as verified by ad hoc simulations. These preliminary results, showing areas of the volcanic edifice experiencing high deformation, can improve the determination of the sources of deformations, i.e. the most relevant problem in the volcano monitoring. Moreover, the knowledge of the deformation pattern, including the topography effects, can provide significant indications to optimize the location of sensors and the characteristics needed to design an efficient and reliable geodetic monitoring network able to detect shallow intrusion events. |
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