The surface-waves methods are well-established techniques for subsurface S-wave velocity (VS) reconstruction. Recently, the sensitivity of surface-wave skin depth to Poisson ratio was applied to also estimate P-wave velocity (VP) models from surface-wave records. We use this technique within the framework of three surface-wave methods, the wavelength/depth data transform, the laterally constrained inversion, and surface-wave tomography to estimate both VS and VP models. We apply these methods to a 3-D test data set from a mining site that is characterized by stiff material and by significant elevation contrast. The data were recorded using a regular grid of receivers and an irregular source layout. Pseudo 3D VS and VP models were obtained down to 140 m depth over approximately 900 × 1500 m2 area. The estimated models from the methods well-match the geological information available for the site. Less than 6 % difference is observed between the estimated VS models from the three methods, whereas this value is 7.1 % for the retrieved VP models. The different methods are critically compared in terms of resolution and efficiency.

Seismic surface and body wave analyses are powerful tools for the geotechnical characterization of sites. The use of landstreamers facilitates the acquisition of dense data sets over large areas. However, efficient processing workflows are needed to estimate 3D velocity models from these massive data sets. For surface wave analysis, the manual picking of dispersion curves (DCs) of large data sets is very time-consuming, whereas the accuracy can be biased by operator choices. We apply a semi-automatic workflow for the analysis, processing, and interpretation of a large-scale landstreamer data set acquired for engineering purposes in the Middle East. The workflow involves the application of a validated automatic DC picking algorithm, and the transformation of the DCs into S- and P-wave velocity models through the wavelength-depth technique. The method has a high level of automation, is data driven and does not require extensive data inversion. Another remarkable benefit is that the auto-picking is more than 1,000 times more efficient than standard manual picking and the estimated velocities are in good agreement with available geotechnical and geophysical information. We conclude that the semi-automatic approach may represent a fast and straightforward method suitable for both research and industrial projects, thus enhancing further collaborations and developments.

The acquisition of seismic exploration data in remote locations presents several logistical and economic criticalities. The irregular distribution of sources and/or receivers facilitates seismic acquisition operations in these areas. A convenient approach is to deploy nodal receivers on a regular grid and to use sources only in accessible locations, creating an irregular source–receiver layout. It is essential to evaluate, adapt, and verify processing workflows, specifically for near-surface velocity model estimation using surface-wave analysis, when working with these types of datasets. In this study, we applied three surface-wave techniques (i.e., wavelength–depth (W/D) method, laterally constrained inversion (LCI), and surface-wave tomography (SWT)) to a large-scale 3D dataset obtained from a hard-rock site using the irregular source–receiver acquisition method. The methods were fine-tuned for the data obtained from hard-rock sites, which typically exhibit a low signal-to-noise ratio. The wavelength–depth method is a data transformation method that is based on a relationship between skin depth and surface-wave wavelength and provides both S- and P-wave velocity (V _s and V _p) models. We used Poisson’s ratios estimated through the wavelength–depth method to constrain the laterally constrained inversion and surface-wave tomography and to retrieve both V _s and V _p also from these methods. The pseudo-3D V _s and V _p models were obtained down to 140 m depth over an area of approximately 900 × 1500 m ². The estimated models from the methods matched the geological information available for the site. A difference of less than 6 % was observed between the estimated V _s models from the three methods, whereas this value was 7.1 % for the retrieved V _p models. The methods were critically compared in terms of resolution and efficiency, which provides valuable insights into the potential of surface-wave analysis for estimating near-surface models at hard-rock sites.