GNGTS 2018 - 37° Convegno Nazionale

700 GNGTS 2018 S essione 3.2 Bibliografia Dimitrakopoulos, R., Luo, X., 2004. Generalized sequential Gaussian simulation on group size ν and screen-effect approximations for large field simulations. Mathematical Geology, 36(5), pp.567-591. Hansen, T.H., Vu, L.T., Bach T.; 2016: MPSLIB: A C++ class for sequential simulation of multiple-point statistical models. SoftwareX, 5, pp. 127-133. Høyer, A.S., Vignoli, G., Hansen, T.M., Keefer, D.A., Jørgensen, F.; 2017: Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies. Hydrology and Earth Sy- stem Sciences, 21(12), p.6069. Journel, A.G., Huijbregts, C.J.; 1978: Mining geostatistics (Vol. 600). London: Academic press. Journel, A., Zhang, T.; 2006: The necessity of a multiple-point prior model. Mathematical geology, 38(5), pp.591-610. Liu, Y.; 2006: Using the Snesim program for multiple-point statistical simulation, Comput. Geosci., 32, pp. 1544– 1563. Matheron, G.; 1973: The intrinsic random functions and their applications. Advances in applied probability, 5(3), pp.439-468. Seifert, D., Jensen, J.L.; 1999: Using sequential indicator simulation as a tool in reservoir description: issues and uncertainties. Mathematical Geology, 31(5), pp.527-550. HYDROLOGICAL MODEL FOR RETRIEVING SUBSIDENCE VELOCITY FROM GPS DATA: APPLICATION TO PO DELTA AREA E. Vitagliano 1 , R. Di Maio 1 , E. Piegari 1 , U. Riccardi 1 , J.P. Boy 2 , M. Fabris 3 , V. Achilli 3 1 Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse, Università di Napoli Federico II, Napoli, Italy 2 EOST-IPGS - Université de Strasbourg, Strasbourg, France 3 Dipartimento di Ingegneria Civile, Edile ed Ambientale, Università di Padova, Padova, Italy Introduction. Suitable estimation of the land subsidence is crucial in deltas and coastal areas, where the climate variability effects (e.g., frequent and intense rain storms, peaks in river discharge and sea level fluctuations), coupled with natural or anthropogenic land sinking, represent serious factors of inundation risk. Nowadays, continuous GNSS (Global Navigation Satellite Systems) networks have become precisionmonitoring tools of the ground displacement. Many site-position time series exhibit a linear trend plus seasonal oscillations of annual and semi-annual periods. These periodic variations may be due to the joint contributions of surface mass redistribution (atmosphere, ocean, snow and soil moisture), frequently masked by the superposition of several correlated or uncorrelated noise sources (Bock and Melgar, 2016). These signals have to be recognised and properly modelled in order to estimate the vertical ground lowering. Several authors investigate the seasonal component by using the “peering approach”, which is based on the comparison between the joint contribution of established individual geophysical sources (not removed during the data processing phase) and the observed seasonal variations (Dong et al. , 2002). This approach allows to quantify influence, distribution and magnitude of the individual sources and to understand the main processes affecting the geodetic time series. Differently from this approach, we propose a procedure based on two steps: multi-disciplinary and multi-methodological comparative analyses are first performed for selecting the physical mechanism (individual source) that better explain the seasonal signals clearly exhibited by the GPS (Global Positioning System) time series. Then, a physically-based model is used for enhancing the extraction of the geodetic trend and thus estimating the geodetic velocity without seasonal oscillations. The proposed approach is applied to the Codigoro Area, for the time span ranging from 2009 to 2017. Codigoro is located in the southern part of the Po Delta – Northern Italy (Fig. 1), an area historically affected by both anthropogenic (Bondesan and Simeoni, 1983) and natural