Today, surface-wave analysis is widely adopted for building near-surface S-wave velocity models. The surface-wave method is under continuous and rapid evolution, also thanks to the lively scientific debate among different disciplines, and interest in the technique has increased significantly during the last decade. A comprehensive review of the literature in the main scientific journals provides historical perspective, methodological issues, applications, and most-promising recent approaches. Higher modes in the inversion and retrieval of lateral variations are dealt with in great detail, and the current scientific debate on these topics is reported. A best-practices guideline is also outlined.

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Seismic reflection data contain surface waves that can be processed and interpreted to supply shear-wave velocity models along seismic reflection lines. The coverage of seismic reflection data allows the use of automated multifold processing to extract high-quality dispersion curves and experimental uncertainties in a moving spatial window. The dispersion curves are then inverted using a deterministic, laterally constrained inversion to obtain a pseudo-2D model of the shear-wave velocity. A Monte Carlo global search inversion algorithm optimizes the parameterization. When the strategy is used with synthetic and field data, consistent final models with smooth lateral variations are successfully retrieved. This method constitutes an improvement over the individual inversion of single dispersion curves.

Inversion of surface wave data suffers from solution non‐uniqueness and is hence strongly biased by the initial model. The Monte Carlo approach can handle this non‐uniqueness by evidencing the local minima but it is inefficient for high dimensionality problems and makes use of subjective criteria, such as misfit thresholds, to interpret the results. If a smart sampling of the model parameter space, which exploits scale properties of the modal curves, is introduced the method becomes more efficient and with respect to traditional global search methods it avoids the subjective use of control parameters that are barely related to the physical problem. The results are interpreted drawing inference by means of a statistical test that selects an ensemble of feasible shear wave velocity models according to data quality and model parameterization. Tests on synthetic data demonstrate that the application of scale properties concentrates the sampling of model parameter space in high probability density zones and makes it poorly sensitive to the initial boundary of the model parameters. Tests on synthetic and field data, where boreholes are available, prove that the statistical test selects final results that are consistent with the true model and which are sensitive to data quality. The implemented strategies make the Monte Carlo inversion efficient for practical applications and able to effectively retrieve subsoil models even in complex and challenging situations such as velocity inversions.

In several domains of applied geophysics, surface, and guided waves are considered as a source of information for characterizing the near surface, which in a marine environment includes the seabed. By contrast, in exploration seismic surveys, these waves have traditionally been regarded as coherent noise that should be filtered out as soon as possible. The authors consider that surface and guided waves are not noise but a signal that can be lifted from the seismic record and exploited for a variety of well-established geophysical solutions. Surface and guided waves constitute a large part of the recorded energy and with proper acquisition, analysis, and inversion they can be used to characterize the near surface with surprisingly high resolution. In this role, they can provide valuable information for tasks such as perturbation correction—adjustment for near-surface traveltime distortions. They can also be used for velocity and geological modeling. In this article, the authors discuss a workflow for the analysis and joint inversion of surface and guided waves in both land and offshore seismic data.

Surface-wave techniques are mainly used to retrieve 1D subsurface models. However, in 2D environments, the 1D approach usually neglects the presence of lateral variations and because the surface-wave path crosses different materials, the resulting model is a simplified or misleading description of the site. We tested a processing technique to retrieve 2D structures from surface-wave data acquired with a limited number of receivers. Our technique was based on a two-step process. First, we extracted several local dispersion curves along the survey line using a spatial windowing based on a set of Gaussian windows with different shapes; the window maxima span the survey line so that we were able to extract a dispersion curve from the seismic record for every window. This provided a set of local dispersion curves each of them referring to a different subsurface portion. This space varying spatial windowing provided a good compromise between wavenumber resolution and the lateral resolution of the obtained local dispersion curves. In the second step, we inverted the retrieved set of dispersion curves using a laterally constrained inversion scheme. We applied this procedure to the processing of synthetic and real data sets and the method proved to be successful in reconstructing even complex 2D structures in the subsurface.

A B S T R A C TSurface wave analysis is usually applied as a 1D tool to estimate V S profiles. Here we evaluate the potential of surface wave analysis for the case of lateral variations. Lateral variations can be characterized by exploiting the data redundancy of the ground roll contained in multifold seismic data. First, an automatic processing procedure is applied that allows stacking dispersion curves obtained from different records and which retrieves experimental uncertainties. This is carried out by sliding a window along a seismic line to obtain an ensemble of dispersion curves associated to a series of spatial coordinates. Then, a laterally constrained inversion algorithm is adopted to handle 2D effects, although a 1D model has been assumed for the forward problem solution. We have conducted different tests on three synthetic data sets to evaluate the effects of the processing parameters and of the constraints on the inversion results. The same procedure, applied to the synthetic data, was then tested on a field case. Both the synthetic and field data show that the proposed approach allows smooth lateral variations to be properly retrieved and that the introduction of lateral constraints improves the final result compared to individual inversions.

We implemented a joint inversion method to build P-and S-wave velocity models from Rayleigh-wave and P-wave refraction data, specifically designed to deal with laterally varying layered environments. A priori information available over the site and any physical law to link model parameters can be also incorporated. We tested and applied the algorithm behind the method. The results from a field data set revealed advantages with respect to individual surface-wave analysis (SWA) and body wave tomography (BWT). The algorithm imposed internal consistency for all the model parameters relaxing the required a priori assumptions (i.e., Poisson's ratio level of confidence in SWA) and the inherent limitations of the two methods (i.e., velocity decreases for BWT).