Retrieving 2D structures from surface-wave data by means of space-varying spatial windowing

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. EN39-EN51 ◽  
Author(s):  
Paolo Bergamo ◽  
Daniele Boiero ◽  
Laura Valentina Socco
Keyword(s):  
Surface Wave ◽  
Real Data ◽  
Processing Technique ◽  
Dispersion Curves ◽  
Data Sets ◽  
Local Dispersion ◽  
Wave Data ◽  
Survey Line ◽  
Wave Path ◽  
Surface Wave 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.

scholarly journals Time-lapse monitoring of climate effects on earthworks using surface waves

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. EN1-EN15 ◽  
Author(s):  
Paolo Bergamo ◽  
Ben Dashwood ◽  
Sebastian Uhlemann ◽  
Russell Swift ◽  
Jonathan E. Chambers ◽  
...  
Keyword(s):  
Surface Wave ◽  
Transportation Network ◽  
Wave Dispersion ◽  
Time Lapse ◽  
Spatial And Temporal Variability ◽  
Data Sets ◽  
Initial Model ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Surface Wave Data

The UK’s transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained [Formula: see text] sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed [Formula: see text] models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.

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

2021 ◽  
Author(s):  
Giulio Vignoli ◽  
Julien Guillemoteau ◽  
Jeniffer Barreto ◽  
Matteo Rossi
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Dispersion Curves ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Wave Data ◽  
Surface Wave Data ◽  
Depth Of Investigation

Summary The analysis of surface wave dispersion curves is a way to infer the vertical distribution of shear-wave velocity. The range of applicability is extremely wide: going, for example, from seismological studies to geotechnical characterizations and exploration geophysics. However, the inversion of the dispersion curves is severely ill-posed and only limited efforts have been put in the development of effective regularization strategies. In particular, relatively simple smoothing regularization terms are commonly used, even when this is in contrast with the expected features of the investigated targets. To tackle this problem, stochastic approaches can be utilized, but they are too computationally expensive to be practical, at least, in case of large surveys. Instead, within a deterministic framework, we evaluate the applicability of a regularizer capable of providing reconstructions characterized by tunable levels of sparsity. This adjustable stabilizer is based on the minimum support regularization, applied before on other kinds of geophysical measurements, but never on surface wave data. We demonstrate the effectiveness of this stabilizer on: i) two benchmark—publicly available— datasets at crustal and near-surface scales; ii) an experimental dataset collected on a well-characterized site. In addition, we discuss a possible strategy for the estimation of the depth of investigation. This strategy relies on the integrated sensitivity kernel used for the inversion and calculated for each individual propagation mode. Moreover, we discuss the reliability, and possible caveats, of the direct interpretation of this particular estimation of the depth of investigation, especially in the presence of sharp boundary reconstructions.

scholarly journals A Reduced Order Approach for Probabilistic Inversions of 3DMagnetotelluric Data II: Joint inversion of MT and Surface-Wave Data

2021 ◽  
Author(s):  
María Constanza Manassero ◽  
Juan Carlos Afonso ◽  
Fabio Iván Zyserman ◽  
Sergio Zlotnik ◽  
Ilya Fomin
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Reduced Order ◽  
Wave Data ◽  
Surface Wave Data

Focal mechanism and source depth of earthquakes from body- and surface-wave data

1971 ◽  
Vol 61 (5) ◽  
pp. 1369-1379 ◽  
Author(s):  
Nezihi Canitez ◽  
M. Nafi Toksöz
Keyword(s):  
Surface Wave ◽  
Body Wave ◽  
Source Parameters ◽  
Focal Depth ◽  
Spectral Ratio ◽  
The Body ◽  
Spectral Ratios ◽  
Motion Data ◽  
Wave Data ◽  
Surface Wave Data

abstract The determination of focal depth and other source parameters by the use of first-motion data and surface-wave spectra is investigated. It is shown that the spectral ratio of Love to Rayleigh waves (L/R) is sensitive to all source parameters. The azimuthal variation of the L/R spectral ratios can be used to check the fault-plane solution as well as for focal depth determinations. Medium response, attenuation, and source finiteness seriously affect the absolute spectra and introduce uncertainty into the focal depth determinations. These effects are nearly canceled out when L/R amplitude ratios are used. Thus, the preferred procedure for source mechanism studies of shallow earthquakes is to use jointly the body-wave data, absolute spectra of surface waves, and the Love/Rayleigh spectral ratios. With this procedure, focal depths can be determined to an accuracy of a few kilometers.

Effects of thin soft layers on body waves

1969 ◽  
Vol 59 (5) ◽  
pp. 2071-2078
Author(s):  
Tom Landers ◽  
Jon F. Claerbout
Keyword(s):  
Surface Wave ◽  
Upper Mantle ◽  
Body Wave ◽  
Wave Dispersion ◽  
Soft Material ◽  
Body Waves ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Use Of Time ◽  
Surface Wave Data

abstract The inability of simple layered models to fit both Rayleigh wave and Love wave data has led to the proposal of an upper mantle interleaved with thin soft horizontal layers. Since surface-wave dispersion is not sensitive to the distribution of soft material but only to the fraction of soft material a variety of models is possible. The solution to this indeterminancy is found through body-wave analysis. It is shown that body waves are dispersed according to the thinness and softness of the layers. Three models, each of which satisfy all surface-wave data, are examined. Transmission seismograms calculated for these models show one to be impossible, one improbable and the other possible. Synthesis of the seismograms is accomplished through the use of time domain theory as the complicated frequency response of the models makes a frequency oriented Haskell-Thompson approach impractical.

Stacking of multilayout and multichannel surface-wave data on the f-k domain

2018 ◽  
Vol 159 ◽  
pp. 93-107 ◽  
Author(s):  
Maria João Coelho ◽  
Jaime Santos ◽  
Marília Pereira
Keyword(s):  
Surface Wave ◽  
Wave Data ◽  
Surface Wave Data

Measurement of interstation phase and group velocities and Q using Wiener filtering

1982 ◽  
Vol 72 (1) ◽  
pp. 73-91
Author(s):  
Steven R. Taylor ◽  
M. Nafi Toksöz
Keyword(s):  
Surface Wave ◽  
Green’S Function ◽  
Green's Function ◽  
Amplitude Spectrum ◽  
Wiener Filter ◽  
Wave Path ◽  
Phase And Group Velocities ◽  
Filtering Technique ◽  
Surface Wave Data ◽  
Group Velocities

abstract A method for calculating interstation phase and group velocities and attenuation coefficients using a Wiener (least-squares) filtering technique is presented. The interstation Green's (or transfer) function is estimated from surface wave data from two stations laying along the same great circle path. The estimate is obtained from a Wiener filter which is constructed to estimate the signal recorded at the station further from the source from the signal recorded at the nearer station. The interstation group velocity is obtained by applying the multiple-filtering technique to the Green's function, and the interstation phase velocity from the phase spectrum of the Green's function. The amplitude spectrum of the Green's function is used to calculate average attenuation between the two stations. Using synthetic seismograms contaminated by noise, it is shown that the Q values calculated from the Green's function are significantly more stable and accurate than those obtained by taking spectral ratios. The method is particularly useful for paths involving short station separations and is applied to a surface wave path crossing the Iranian Plateau.

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