Can the compressed sensing theory be utilized in active source surface wave exploration? A primary result

Abstract Active source surface wave exploration is a crucial technique for delineating shallow underground structures, widely utilized in geological engineering, urban geology, environmental geology, geological hazard assessment, and related fields. Seismic data acquisition plays a pivotal role in active source surface wave exploration techniques, which accounts for most costs. Consequently, research into low-cost acquisition methods holds great significance for active source surface wave exploration. Compressed sensing theory, a novel sampling paradigm, has been proven to facilitate cost-saving measures in certain geophysical prospecting techniques. However, its potential has not yet been investigated within the realm of surface wave exploration. This study explores the feasibility of applying compressed sensing theory in shallow seismic surface wave exploration. An edge-preservation piecewise random sampling method is employed as the compressed sensing sampling strategy, and data reconstruction is accomplished through a damped rank-reduction technique. The fully sampled and reconstructed data undergo identical surface wave data processing procedures, including dispersion spectrum calculation, and dispersion curve inversion. Simulated and field surface wave data experiments demonstrate that the reconstructed data obtained via compressed sensing theory can achieve comparable results to conventionally sampled data. Based on this theory, it is possible to significantly reduce the requisite number of equipment and field workload, making it a promising way for shallow subsurface structure detection.