Layer Stripping FWI for Surface Waves Isabella Masoni ∗ , Total E&P , R. Brossier, University Grenoble Alpes, J. L. Boelle, Total E&P, J. Virieux, University Grenoble Alpes SUMMARY In this study, an innovative layer stripping approach for FWI specifically adapted to the physics of surface waves is inves- tigated, to mitigate the cycle skipping problem. A combined high-to-low frequency filtering with gradually increasing off- set ranges, are applied to observed and calculated data to up- date gradually deeper layers of the shear velocity model. Suc- cessful results for a synthetic data example are presented. INTRODUCTION The construction of subsurface velocity models is an ongoing issue for oil and gas exploration. For land and shallow ma- rine acquisitions, topography and weathered or unconsolidated top layers can lead to a very complex near surface, that can cause problems for the imaging of deeper exploration targets due to the presence of groundroll. In such cases, an innovative characterization of near surface properties is needed. Surface waves, conventionally considered as noise, sample this shal- low zone, and may provide information on the velocity het- erogeneities present. As a high-resolution imaging technique, waveform inversion allows extension beyond the locally lay- ered assumption of conventional surface wave imaging meth- ods. MOTIVATION The generic FWI formalism does not rely on a specific wave type. In practice however, success with FWI has mainly ex- ploited body waves under an acoustic approximation of wave- propagation. Although some elastic FWI applications have been performed using body waves, the use of surface waves is still challenging (Brossier et al., 2009). When considering slow surface waves propagating in the low velocity medium of the near surface, finding a sufficiently ac- curate initial model is essential for avoiding local minima. If the initial data do not predict the observed data with an er- ror smaller than half a period, the optimization goes to a local minimum due to cycle-skipping (Mulder and Plessix, 2008). One way to tackle this issue is to implement more robust mis- fit functions, such as an envelope misfit (Yuan et al., 2015), or taking advantage of alternative data domains (P´ erez Solano et al., 2014; Masoni et al., 2014). In addition multiscale ap- proaches such as wavelet decomposition (Yuan et al., 2015) or conventional frequency continuation approachs (Bunks et al., 1995; Sirgue and Pratt, 2004), are used to invert low-to-high frequency content, updating first the large-scale structure, and then the more detailed features of the velocity model. This study investigates an innovative layer stripping approach for FWI specifically adapted to the physics of surface waves. Layer stripping is a well known approach used in inversion methods (Gibson et al., 2009; Shi et al., 2015), in which the model is recovered layer by layer in a top-to-bottom manner. The frequency content of surface waves is directly related to their penetration depth. Surface waves of higher frequency and shorter wavelength will therefore sample the top layers of a medium, while waves with lower frequencies and longer wavelengths will sample as well deeper parts of a medium. This can be observed by analyzing frequency gradients (Fig- ure 1). This suggests an inversion workflow from high-to-low frequency content, leading to a layer-stripping FWI approach. (a) (b) 0 3 6 9 depth (m) 0 10 20 30 40 50 distance (m) Sensitivity for frequency band 35-55 Hz 0 3 6 9 depth (m) 0 10 20 30 40 50 distance (m) Sensitivity for frequency band 70-110 Hz Figure 1: Data gradients over frequency bands 70 − 110 Hz (a) and 35 − 55 Hz (b). One can observe that lower frequency band samples the model at greater depths, but it is also of lower resolution (same color scale used for (a) and (b)). In this study, the synthetic model from P´ erez Solano et al. (2014) (Figure 6d) is used to test and evaluate layer stripping FWI for surface waves. The S-wave and P-wave velocity mod- els are related by a constant poisson ratio (VP/VS = 2.0), with a homogeneous density model of ρ = 1000 kgm −3 . Two high velocity anomalies, at the center of the model, are the targets of the inversion. Elastic 2D wave propagation is simulated using a finite difference method. The synthetic data (Figure 5a) is recorded by 145 vertical and horizontal component receivers, at 0.2 m below the surface. 20 vertical sources, positioned 0.2 m below the surface are simulated using a 40 Hz Ricker as the source wavelet. The initial shear velocity model consists of a linear gradient (Figure 4), and is the only parameter to be in- verted. The true P-wave and density models, as well as the true source signature, are used. The focus is on the exploitation of the surface waves, which dominate the data in amplitude and are the main wavefield component driving misfit minimization. These contain information on the shear velocity properties of the medium. The initial data is heavily cycle-skipped (Figures