Estimating S-wave velocities from 3D 9-component shallow seismic data using local Rayleigh-wave dispersion curves – A field study

Pan, Yudi; Schaneng, S.; Steinweg, T.; Bohlen, Thomas

doi:10.1016/j.jappgeo.2018.09.037

Cited by 24 publications

(18 citation statements)

References 43 publications

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“…Within this context, several methods for constructing 2D/3D Vs models using in-situ measurements have been developed. The simplest approach is to utilize multiple spatially distributed 1D Vs profiles to construct pseudo-2D (e.g., Luo et al, 2008;Mi et al, 2017;Parolai et al, 2001) and 3D (e.g., Pan et al, 2018;Piatti et al, 2013;Strobbia et al, 2011) Vs models by spatial interpolation techniques. For example, this approach has been applied to construct pseudo-2D Vs profiles from multichannel analysis of surface waves (MASW) by interpolating a series of 1D Vs models derived from inversion of waveforms collected using different receiver arrays (e.g., Ismail et al, 2014;Mahvelati and Coe, 2017;Park, 2005).…”

Section: Introductionmentioning

confidence: 99%

An H/V Geostatistical Approach for Building Pseudo-3D Vs Models to Account for Spatial Variability in Ground Response Analyses I: Model Development

Hallal¹,

Cox²

2020

Preprint

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Many recent studies have shown that we are generally unable to accurately replicate recorded ground motions at most borehole array sites using available subsurface geotechnical information and one-dimensional (1D) ground response analyses (GRAs). When 1D GRAs fail to accurately predict recorded site response, the site is often considered too complex to be effectively modeled as 1D. While 3D numerical GRAs are possible and believed to be more accurate, there is rarely a 3D subsurface model available for these analyses. The lack of affordable and reliable site characterization methods to quantify spatial variability in subsurface conditions, particularly regarding shear wave velocity (Vs) measurements needed for GRAs, has pushed researchers to adopt stochastic approaches, such as Vs randomization and spatially correlated random fields. However, these stochastically generated models require the assumption of generic, or assumed, input parameters, introducing significant uncertainties into the site response predictions. This paper describes a new geostatistical approach that can be used for building pseudo-3D Vs models as a means to rationally account for spatial variability in GRAs, increase model accuracy, and reduce uncertainty. Importantly, it requires only a single measured Vs profile and a number of simple, cost-effective, horizontal-to-vertical spectral ratio (H/V) noise measurements. Using Gaussian geostatistical regression, irregularly sampled estimates of fundamental site frequency from H/V measurements (f0,H/V) are used to generate a uniform grid of f0,H/V across the site with accompanying Vs profiles that have been scaled to match each f0,H/V value, thereby producing a pseudo-3D Vs model. This approach is demonstrated at the Treasure Island and Delaney Park Downhole Array sites (TIDA and DPDA, respectively). While the pseudo-3D Vs models can be used to incorporate spatial variability into 1D, 2D, or 3D GRAs, their implementation in 1D GRAs at TIDA and DPDA is discussed in a companion paper.

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Section: Introductionmentioning

confidence: 99%

An H/V Geostatistical Approach for Building Pseudo-3D Vs Models to Account for Spatial Variability in Ground Response Analyses I: Model Development

Hallal¹,

Cox²

2020

Preprint

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“…To demonstrate the advantages of the complex‐vector method further, the stacking method (Pan et al . ) for multicomponent Rayleigh waves is compared. The stacked image by the radial and vertical components is shown in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Pan et al . () proposed stacking dispersive images from different components. However, the stacked dispersion image sometimes becomes worse when one of the components is polluted by strong interferences.…”

Section: Introductionmentioning

confidence: 99%

Rayleigh‐wave dispersion analysis using complex‐vector seismic data

Qiu

Wang

2019

Near Surface Geophysics

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A B S T R A C TIdentification of different modes of Rayleigh waves is essential in surface-wave surveys. Multi-mode Rayleigh waves can provide higher accuracy of the near-surface structure than the fundamental mode alone. However, some modes or frequencies of Rayleigh waves may be absent in the vertical-component seismic data. To complement the dispersion information, a method based on complex-vector seismic data is proposed. We construct the complex vector by setting the radial component and vertical component as the real part and imaginary part, respectively. Then, high-resolution linear Radon transform is used to obtain the multi-mode Rayleighwave dispersion image of the complex-vector seismic data. Based on different dispersion characteristics of the radial and vertical components, the dispersion images of the complex-vector seismic data show better performance against interferences and mode misidentification. Synthetic and field examples demonstrate advantages of the complex-vector method over the traditional vertical-component method in spectral bands and dispersion curve mode identification. Therefore, a more robust and accurate near-surface S-wave velocity structure can be expected compared to the traditional vertical-component Rayleigh-wave method.

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“…Many algorithms have been proposed over the years to invert surface-wave dispersion curves, including both local search methods based on the gradient or Jacobian matrix (Fréchet derivative) (e.g., Xia et al 1999) and global search methods that solve the optimization problem stochastically (e.g., Yamanaka and Ishida 1996;Socco and Boiero 2008;Boaga et al 2011;Song et al 2012). By interpolating multiple 1D profiles estimated from different spreads using MASW, a pseudo-2D (Bohlen et al 2004;Luo et al 2009;Mi et al 2017) or 3D model (Boiero et al 2011;Strobbia et al 2011;Ikeda and Tsuji 2015;Pan et al 2016aPan et al , 2018b can then be reconstructed. Since each picked dispersion curve represents a harmonic mean of the velocity structure below the spread , the lateral heterogeneity would be partly reconstructed if the lateral changes in the dispersion curves could be estimated accurately (Mi et al 2017).…”

Section: Maswmentioning

confidence: 99%

“…17c). This 2D result was obtained by combining a total of 16 1D S-wave velocity profiles, which were estimated by a moving boxcar spatial filter of 16 m (Pan et al 2018b). The size of the resulting 2D MASW model is smaller than the MFWI due to the 1D approximation, that means we only estimated one 1D structure every 16 m. The 2D MASW result only shows weak lateral variations in the model and is unable to delineate the shape of the trench.…”

Section: An Example In Rheinstetten Germanymentioning

confidence: 99%

High-Resolution Characterization of Near-Surface Structures by Surface-Wave Inversions: From Dispersion Curve to Full Waveform

2019

Self Cite

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Surface waves are widely used in near-surface geophysics and provide a non-invasive way to determine near-surface structures. By extracting and inverting dispersion curves to obtain local 1D S-wave velocity profiles, multichannel analysis of surface waves (MASW) has been proven as an efficient way to analyze shallow-seismic surface waves. By directly inverting the observed waveforms, full-waveform inversion (FWI) provides another feasible way to use surface waves in reconstructing near-surface structures. This paper provides a state of the art on MASW and shallow-seismic FWI, and a comparison of both methods. A two-parameter numerical test is performed to analyze the nonlinearity of MASW and FWI, including the classical, the multiscale, the envelope-based, and the amplitude-spectrum-based FWI approaches. A checkerboard model is used to compare the resolution of MASW and FWI. These numerical examples show that classical FWI has the highest nonlinearity and resolution among these methods, while MASW has the lowest nonlinearity and resolution. The modified FWI approaches have an intermediate nonlinearity and resolution between classical FWI and MASW. These features suggest that a sequential application of MASW and FWI could provide an efficient hierarchical way to delineate near-surface structures. We apply the sequential-inversion strategy to two field data sets acquired in Olathe, Kansas, USA, and Rheinstetten, Germany, respectively. We build a 1D initial model by using MASW and then apply the multiscale FWI to the data. High-resolution 2D S-wave velocity images are obtained in both cases, whose reliabilities are proven by borehole data and a GPR profile, respectively. It demonstrates the effectiveness of combining MASW and FWI for high-resolution imaging of near-surface structures.

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Estimating S-wave velocities from 3D 9-component shallow seismic data using local Rayleigh-wave dispersion curves – A field study

Cited by 24 publications

References 43 publications

An H/V Geostatistical Approach for Building Pseudo-3D Vs Models to Account for Spatial Variability in Ground Response Analyses I: Model Development

An H/V Geostatistical Approach for Building Pseudo-3D Vs Models to Account for Spatial Variability in Ground Response Analyses I: Model Development

Rayleigh‐wave dispersion analysis using complex‐vector seismic data

High-Resolution Characterization of Near-Surface Structures by Surface-Wave Inversions: From Dispersion Curve to Full Waveform

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