Assessment and Validation of Surface Wave Data Processing Tools within Reveal

In past decades, Surface Wave Methods (SWM) became powerful tools for near-surface characterisation. They are mainly used to retrieve a 1D shear wave velocity model or to estimate the Vs,30 (average shear-wave velocity in the topmost 30 m of the subsurface) at a site. However, the existing techniques used to characterise their dispersion properties remain limited. This thesis focuses on the capabilities and limitations of newly developed tools for surface wave analysis within the Shearwater Reveal software. Taking advantage of the software’s flexibility, we systematically test and evaluate the performance of eight experimental modules: SWAMoveOut, SWAWaveFormTrans, SWAFFT, SWFK, SWAPicking, SWATAUMOD, SWADeltaPhi, SWTomo, SWDinv, and SWI. Two real-world 3D active-source seismic datasets acquired in the same geological conditions using two source configurations (low- and high-frequency sweeps) are used to assess the tools. The processing is divided into three main steps: (1) spectral transformation into the f-k domain, (2) surface wave tomography to investigate lateral phase velocity variations, and (3) 1D inversion to retrieve a shear-wave velocity subsurface model. Particular focus is placed on modal identification, spectral energy distribution, and the impact of source frequency content on results reliability. Through this study, we identify key strengths such as well-designed preprocessing tools, as well as enhanced modal separation via SWFK/SWFFT, and the multifunctionality of tomography and depth inversion tools. The challenges include computational limitations and tool sensitivity to input parameters. Recommendations for future development focus on improving algorithmic stability, such as phase-extraction and SWI tools, and making them more user-friendly. Ultimately, this work confirms practical validation of software’s potential for Surface Wave Analysis (SWA) and leads to a broader discussion on the integration of surface wave analysis into conventional seismic workflows. The findings highlight the significant, yet often underutilized, information content carried by surface waves and offer a roadmap for extracting this value with modern processing tools.