Near-Surface Model Prediction and Refinement by Full Waveform Surface Wave Inversion

Genetic algorithm full waveform inversion (GA-FWI) is able to predict complex shear-wave velocity (Vs) models fairly from surface waves, even in the case when very limited or null a-priori information is available (Xing and Mazzotti, 2017a). Out of consideration for computing time reduction, a two-grid approach (Sajeva et al., 2016; Aleardi and Mazzotti, 2017; Mazzotti et al., 2017), one coarse grid for the inversion and one small grid for the modeling, is recommended for the method. Thus, we generally obtain smooth velocity models whose wavelengths are dependent on the coarse grid spacing. In this paper, we show that these models are suitable starting models for FWI approaches with local optimization methods and that, in general, significant details of the depth model can be retrieved. Instead, we do not discuss the influences caused by different surface wave modeling strategies (Thorbecke and Draganov, 2011; Groos, 2013; Xing and Mazzotti, 2016) and by assumptions in wave modeling (Xing and Mazzotti, 2017b), thus we focus on model prediction and refinement