GNGTS 2016 - Atti del 35° Convegno Nazionale

496 GNGTS 2016 S essione 3.1 recorded offset of 7.5 km and two expanding spread experiments, located at the beginning and at the end of the profile, respectively, that are characterized by a maximum recorded offset of 40 km approximately. The expanding spreads experiments are composite shots where the charge size increases with the source-spread distance to preserve an appropriate signal-to-noise-ratio at far offsets (Stucchi et al. , 2003). The total length of the studied profile is around 50 km and the station elevation ranges between 50 m and 450 m. The receiver interval is 60 m, the sample interval is 2 ms and the record length used is 8 s. In Fig. 1a and Fig. 1b is illustrated one of the long-offset experiments before and after the data enhancement. Figs. 1c and Fig. 1d show an example of raw production shot gather before and after the application of the same steps for data enhancement of Fig. 1b. The gathers in Figs. 1b and 1d are obtained after a processing sequence that include trace muting, F-K filtering, F-X deconvolution and dip scan filtering. The observed data employed in the following FWI are the envelope of the low pass filtered (10 Hz) version of the direct and diving waves. Stochastic full waveform inversion. In the contest of FWI, the numerical solution of the wave equation is required to obtain the predicted data to compare with the observed data. Since we want to model only the direct and diving waves, we use an explicit, 2 nd order in time, finite difference algorithm to solve the 2D acoustic wave equation. The model size is approximately 50 km in the length and 4.5 km in depth. The modeling grid is made by 150 x 1563 (Fig. 2a) nodes with a uniform space sampling of 30 m. Because of numerical stability, we consider a time sampling of 2ms, and due to numerical dispersion, the algorithm models correctly the predicted seismograms only in the frequency range up to 10 Hz. The source wavelet is estimated from the data by means of singular value decomposition. As described in Sajeva et al. (2014) and Tognarelli et al. (2015), in our inversion approach we use a coarse grid that differs from the modelling grid and that is characterized by a non uniform cell size. A bilinear interpolation is applied to bring the velocity model from the inversion grid to the modelling grid. In order to reduce the number of unknowns for the inversion problem, we decrease the number of nodes as a function of the depth, where the illumination is poorer. The whole procedure allows to reduce the total number of unknowns to 120 (red dots in Fig. 2a). The GA parameters are set as follow: 300 individuals that evolve for 300 generations, selection rate 0.8 and mutation rate 0.008. The search ranges Fig. 2 – a) The axis of the plot represent the number of nodes of the modeling grid. The red dots are the nodes considered in the inversion grid. On top, the black line refers to the topography; b) final velocity model obtained after 300 generations. The model is shown in the modeling grid.