Answers are grounded in this paper and cite their sources.
Shows enhanced depth sensitivity in subsurface profiling, indicating improved modeling outcomes.
The reconstruction of two-dimensional (2D) subsurface shear-wave velocity (V S ) structures remains challenging when relying on surface-based seismic methods, due to limited depth penetration and the intrinsic non-uniqueness associated with the frequent reliance on a single observable. Here we present an efficient technique for the joint acquisition and analysis of multi-component passive seismic data recorded along linear arrays, aimed at obtaining robust subsurface 2D V S models. The method, named 2D SuPPSALA (2D SUbsurface Profiling via Passive Surface-wave data Analysis from Linear Arrays), combines surface-wave dispersion derived from moving subarrays of the deployed array with station-specific spectral ratios within a unified joint inversion framework. The approach can be implemented using either 3-component seismic nodes or traditional cabled systems acquiring vertical and transverse components, provided that sensor response curves are the same. The method is illustrated through a shallow subsurface case study in a perilagoon environment of northeastern Italy, characterized by abrupt lateral variations associated with residual palaeodunes and peat-filled palaeochannels. The resulting 2D V S model clearly images these features, with very low velocities ( < 100 m/s) in peat-rich zones and high velocities (> 300 m/s) in stiff sandy deposits. Independent electromagnetic data show good agreement with the seismic profile, providing mutual validation. Compared to conventional refraction or roll-along surface-wave techniques, the proposed methodology provides enhanced depth sensitivity and reduced ambiguity, while relying on simpler field operations that are easily applicable to kilometer-scale profiles.
No takes yet. Share an insight, caveat, or question.
Giancarlo Dal Moro (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: