GNGTS 2017 - 36° Convegno Nazionale

734 GNGTS 2017 S essione 3.3 available in literature. Considering the significant computational resources required by a probabilistic approach, for the majority of cases local optimization (gradient-based) methods are used. Thus, there are many examples in literature regarding successful deterministic FWI on synthetic models, both in the elastic and in the acoustic approximation. However, when moving from synthetic to real-data, deterministic FWI still remains challenging mainly due to cycle-skipping issues and local minima problems. In order to overcome this limit it is necessary to start the inversion process from an initial model sufficiently close to the global minimum or, at least, located in its “main valley”. The aim of the work is to assess the required quality of the initial model for FWI, in the frame of near-surface targets. In this note we focus on the first preliminary results regarding the forward modelling part; we investigate differences and similarities between a very well- controlled real acquisition and the corresponding numerical simulations. The case study is an area where a perfectly well-known low density sand-body is surrounded by denser geological formations. An initial near-surface model was retrieved from the laterally constrained inversion (LCI) of the dispersion curves (DCs) along a testing line. 3D forward modelling of wave propagation on this model compare promisingly well with experimental data and suggests that this initial model is accurate enough for making FWI converging to the global minimum we are interested in. Field case. The real-dataset was obtained from a dedicated seismic acquisition in the CNR experimental area in Turin (Italy). In particular, we performed two acquisitions (Fig. 1) following respectively a 3D and a 2D pattern. For both acquisitions we used a 8 kg sledgehammer vertical point source and a total of 72 vertical receivers (4.5 Hz) connected to 3 Geode - seismic modules; the time sampling was set to 0.125 ms and the acquisition time to 0.512 s (with a pre-trig of 0.1 s). For the 3D acquisition (Fig. 1 - left side upper corner) we energized in 83 points, stacking from 8 to 10 shots for each source-position. The in line distance between the source-points was 0.75 m, while the receivers interval was 0.5 m. For the 2D acquisition (Fig. 1 - left side lower corner) we energized in 11 positions (stacking again from 8 to 10 shots for each point); 4 of the Fig. 1 - The CNR acquisition geometry. Left side upper corner: the 3D acquisition scheme and the position of the 2D line (not in scale); left side lower corner: the 2D line with the outlined position of 2 nd shot (green circle); right side upper corner: photo of the area with the scheme of the receivers position for the 3D and 2D acquisition; right side lower corner: simple 3D scheme of the model.