Comparison of surface-wave techniques to estimate S- and P-wave velocity models from active seismic data

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 (Vs and Vp) 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 Vs and Vp also from these methods. The pseudo-3D Vs and Vp models were obtained down to 140 m depth over an area of approximately 900 × 1500 m2. 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 Vs models from the three methods, whereas this value was 7.1 % for the retrieved Vp 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.</p

Challenges in shallow target reconstruction by 3D elastic full-waveform inversion - Which initial model?

Elastic full-waveform inversion (FWI) is a powerful tool for high-resolution subsurface multiparameter characterization. However, 3D FWI applied to land data for near-surface applications is particularly challenging because the seismograms are dominated by highly energetic, dispersive, and complex-scattered surface waves (SWs). In these conditions, a successful deterministic FWI scheme requires an accurate initial model. Our study, primarily focused on field data analysis for 3D applications, aims at enhancing the resolution in the imaging of complex shallow targets, by integrating devoted SW analysis techniques with a 3D spectral-element-based elastic FWI. From dispersion curves, extracted from seismic data recorded over a sharp-interface shallow target, we build different initial S-wave (VS) and P-wave (VP) velocity models (laterally homogeneous and laterally variable), using a specific data transform. Starting from these models, we carry out 3D FWI tests on synthetic and field data, using a relatively straightforward inversion scheme. The field data processing before FWI consists of band-pass filtering and muting of noisy traces. During FWI, a weighting function is applied to the far-offset traces. We test 2D and 3D acquisition layouts, with different positions of the sources and variable offsets. The 3D FWI workflow enriches the overall content of the initial models, allowing a reliable reconstruction of the shallow target, especially when using laterally variable initial models. Moreover, a 3D acquisition layout guarantees a better reconstruction of the target's shape and lateral extension. In addition, the integration of model-oriented (preliminary monoparametric FWI) and data-oriented (time windowing) strategies into the main optimization scheme has produced further improvement of the FWI results

P- and S-wave direct static estimation from surface wave dispersion data

Surface wave dispersion can be inverted for S-wave 1D velocity models that can be used for static computation. We here show a method that, without the need of inverting the data, provides a direct estimate of not only S- but also P-wave time average velocity required for static computation. The method is based on the relationship between investigation depth of Rayleigh waves and their wavelength. We prove that this relationship is robust for estimate a set of VS time average velocity models requiring only a set of dispersion curves and the knowledge of one VS model. We also show that this relationship is sensitive to Poisson's ratio and can therefore be used to estimate VP. We successfully apply the method to a synthetic dataset

Advances in surface-wave tomography for near-surface applications

Surface-wave (SW) tomography is a technique that has been widely used in the field of seismology. It can provide higher resolution relative to the classical multichannel SW processing and inversion schemes that are usually adopted for near-surface applications. Nevertheless, the method is rarely used in this context, mainly due to the long processing times needed to pick the dispersion curves as well as the inability of the two-station processing to discriminate between higher SW modes. To make it efficient and to retrieve pseudo-2D/3D S-wave velocity (VS) and P-wave velocity (VP) models in a fast and convenient way, we develop a fully data-driven two-station dispersion curve estimation, which achieves dense spatial coverage without the involvement of an operator. To handle higher SW modes, we apply a dedicated time-windowing algorithm to isolate and pick the different modes. A multimodal tomographic inversion is applied to estimate a VS model. The VS model is then converted to a VP model with the Poisson’s ratio estimated through the wavelength-depth method. We apply the method to a 2D seismic exploration data set acquired at a mining site, where strong lateral heterogeneity is expected, and to a 3D pilot data set, recorded with state-of-the-art acquisition technology. We compare the results with the ones retrieved from classical multichannel analysis

Ambient seismic noise and surface wave tomography of the Thinia Valley (Kefalonia, Greece)

An extensive active and passive seismic survey was carried out in the Thinia Valley (Kefalonia Island, Greece) in May 2022 to investigate the complex geo-structural setting of the area. Surface waves retrieved from the active seismic surveys provided velocity models along the investigated lines. To potentially increase the investigation depth, a passive 2D array of seismic stations continuously acquired ambient seismic noise in the area between the active lines for three days. The passive recordings were preliminary treated for the retrieval of surface waves through a Frequency Domain Beam Forming Method. Few useful events were detected due to the short duration of the acquisition and the lack of strong anthropic noise sources in the area. To increase the coverage, ambient noise interferometry was carried out on the passive data, with successful results. Ambient noise tomography demonstrated to be a valid complementary tool for the retrieval of a subsurface velocity model, when the passive data lack information on the noise source azimuth

Elastic Full Waveform Inversion Tests for Shallow Targets Reconstruction from Surface Waves Analysis Based Initial Models

Seismic data from land acquisitions are dominated by highly energetic surface waves (SW), showing complex behaviour when interacting with local structures. SW analysis is, therefore, an important tool for accurate nearsurface imaging, even if most of the conventional SW analysis techniques are limited by the lateral invariance assumption. This study attempts to enhance the resolution of shallow targets reconstruction by integrating SW dispersion curves (DC) analysis techniques with a spectral element based elastic full-waveform inversion (FWI) workflow. Multi-parameter elastic FWI tests have been conducted over a synthetic dataset related to a benchmark model that mirrors the characteristics of a real test site. The initial S-and P-wave velocity (Vs and Vp) models have been retrieved by DC analysis with increasing detail resolution. The FWI results showed a better model reconstruction when starting from a more detailed initial model, obtained using the full-DC analysis. Further, we improved the initial Vs model (reconstructed from SW information) by a preliminary mono-parameter FWI step. Starting from this improved initial configuration, we obtained more accurate results when performing the multiparameter FWI