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The analysis of surface wave dispersion curves is a way to infer the verticaldistribution of shear-wave velocity. The range of applicability is extremelywide going, for example, from seismological studies to geotechnicalcharacterizations and exploration geophysics. However, the inversion of thedispersion curves is severely ill-posed and only limited efforts have been putinto the development of effective regularization strategies. In particular,relatively simple smoothing regularization terms are commonly used, even whenthis is in contrast with the expected features of the investigated targets. Totackle this problem, stochastic approaches can be utilized, but they are toocomputationally expensive to be practical, at least, in the case of largesurveys. Instead, within a deterministic framework, we evaluate theapplicability of a regularizer capable of providing reconstructionscharacterized by tunable levels of sparsity. This adjustable stabilizer isbased on the minimum support regularization, applied before on other kinds ofgeophysical measurements, but never on surface wave data. We demonstrate theeffectiveness of this stabilizer on i) two benchmark - publicly available -datasets at crustal and near-surface scales, ii) an experimental datasetcollected on a well-characterized site. In addition, we discuss a possiblestrategy for the estimation of the depth of investigation. This strategy relieson the integrated sensitivity kernel used for the inversion and calculated foreach individual propagation mode. Moreover, we discuss the reliability, andpossible caveats, of the direct interpretation of this particular estimation ofthe depth of investigation, especially in the presence of sharp boundaryreconstructions.

Reconstruction, with tunable sparsity levels, of shear-wave velocity profiles from surface wave data