Surface-wave methods are classically used to characterize shear (S-) wave velocities ($V_{\mathrm{S}}$) 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 $V_{\mathrm{S}}$ 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.

中文翻译：

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面波色散的多窗口加权叠加

表面波方法通常用于表征剪切（S-）波速（$V_{\mathrm{小号}}$色散曲线的反演）。当针对具有明显横向异质性的2D浅层结构时，可以实施开窗和堆叠技术以更好地描述$V_{\mathrm{小号}}$横向变化。但是，这些技术在横向分辨率和研究深度（DOI）之间进行权衡，这在使用表面波多通道分析（MASW）方法时是众所周知的。我们采用了一种新颖的方法，旨在通过使用多窗口加权面波叠加（MW-WSSW）来提高MASW的横向分辨率和DOI。MW-WSSW由堆叠从不同大小的数据段获得的色散图像组成，其基于波长的权重取决于数据选择窗口的孔径。从这个意义上讲，MW-WSSW在较小的窗口中为短波长提供了额外的权重，以便更好地告知地下的浅层部分，反之亦然，以提供更高的速度。使用多个窗口可以改善DOI，而应用基于波长的权重则可以提高较浅的横向分辨率。MW-WSSW在开源软件包SWIP中实现，并应用于合成和真实数据集的处理。在这两种情况下，我们都将其与SWIP中已实现的标准开窗和堆叠程序进行了比较。MW-WSSW具有令人信服的结果，具有优化的横向范围，改进的浅层分辨率和增加的DOI。