Multiwindow Weighted Stacking Of Surface-Wave Dispersion

Surface-wave methods are classically used to characterize shear (S-) wave velocities (V-S) of the shallow subsurface through the inversion of dispersion curves. When targeting 2D shallow structures with sharp lateral heterogeneity, windowing and stacking techniques can be implemented to provide a better description of V-S lateral variations. These techniques, however, suffer from the trade-off between lateral resolution and depth of investigation (DOI), which is well-known when using the multichannel analysis of surface waves (MASW) method. We have adopted a novel methodology aimed at enhancing lateral resolution and DOI of MASW results through the use of multiwindow weighted stacking of surface waves (MW-WSSW). MW-WSSW consists of stacking dispersion images obtained from data segments of different sizes, with a wavelength-based weight that depends on the aperture of the data selection window. In that sense, MW-WSSW provides additional weight to short wavelengths in smaller windows so as to better inform shallow parts of the subsurface, and vice versa for deeper velocities. Using multiple windows improves the DOI, whereas applying wavelength-based weights enhances the shallow lateral resolution. MW-WSSW was implemented within the open-source package SWIP and applied to the processing of synthetic and real data sets. In both cases, we compared it with standard windowing and stacking procedures that are already implemented in SWIP. MW-WSSW provided convincing results with optimized lateral extent, improved shallow resolution, and increased DOI.