High‐resolution velocity spectra using eigenstructure methods

Biondi, B.; Kostov, C.

doi:10.1190/1.1892392

Cited by 10 publications

(12 citation statements)

References 2 publications

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“…They can be derived by taking into account the energy of the trace samples selected along trial travel‐time hyperbolic trajectories, they can be evaluated using the signal‐to‐noise (S/N) ratio along the same trajectories, or they can be computed by cross‐correlating the traces in the specific time windows. Many estimators are available with different implementations and acting in different domains (Neidell and Taner ; Jones and Levy ; Sguazzero and Vesnaver ; Biondi and Kostov ; Key and Smithson ; Spagnolini et al . ; Sacchi ; Grandi et al .…”

Section: High‐resolution Velocity Analysis For Deriving a Compressionmentioning

confidence: 99%

Characterisation of shallow marine sediments using high‐resolution velocity analysis and genetic‐algorithm‐driven 1D elastic full‐waveform inversion

Aleardi

Tognarelli

Mazzotti

2016

Near Surface Geophysics

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We estimate the elastic properties of marine sediments beneath the seabed by means of high‐resolution velocity analysis and one‐dimensional elastic full‐waveform inversion performed on two‐dimensional broad‐band seismic data of a well‐site survey. A high‐resolution velocity function is employed to exploit the broad frequency band of the data and to derive the P‐wave velocity field with a high degree of accuracy. To derive a complete elastic characterisation in terms of P‐wave and S‐wave velocities (Vp, Vs) and density of the subsurface, and to increase the resolution of the Vp estimates, we apply a one‐dimensional elastic full‐waveform inversion in which the outcomes derived from the velocity analysis are used as a priori information to define the Vp search range. The one‐dimensional inversion is done using genetic algorithm as the optimisation method. It is performed by considering two misfit functions: the first uses the entire waveform to compute the misfit between modelled and observed seismograms, and the second considers the envelope of the seismograms, thus relaxing the requirement of an exact estimation of the wavelet phase. The full‐waveform inversion and the high‐resolution velocity analysis yield comparable Vp profiles, but the full‐waveform inversion reconstruction is much more detailed. Regarding the full‐waveform inversion results, the final depth models of P‐ and S‐wave velocities and density show a fine‐layered structure with a significant increase in velocities and density at shallow depth, which may indicate the presence of a consolidated layer. The very similar velocities and density‐depth trends obtained by employing the two different misfit functions increase our confidence in the reliability of the predicted subsurface models.

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Section: High‐resolution Velocity Analysis For Deriving a Compressionmentioning

confidence: 99%

Characterisation of shallow marine sediments using high‐resolution velocity analysis and genetic‐algorithm‐driven 1D elastic full‐waveform inversion

Aleardi

Tognarelli

Mazzotti

2016

Near Surface Geophysics

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“…These techniques have been borrowed from the field of sonar processing (Bienvenu and Koop, 1983) and have been successfully applied in geophysics in different contexts, i.e., velocity analysis (Biondi and Kostov, 1989;Kirlin, 1992), wavefield separation (Mari and Glangeaud, 1990;Rutty and Jackson, 1992). Subspace methods can certainly provide high resolution but their performance is severely affected when the number of signals are over or underestimated.…”

Section: Introductionmentioning

confidence: 98%

Estimation of the discrete Fourier transform, a linear inversion approach

Sacchi

Ulrych

1996

GEOPHYSICS

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Spatio-temporal analysis of seismic records is of particular relevance in many geophysical applications, e.g., vertical seismic profiles, plane-wave slowness estimation in seismographic array processing and in sonar array processing. The goal is to estimate from a limited number of receivers the 2-D spectral signature of a group of events that are recorded on a linear array of receivers. When the spatial coverage of the array is small, conventional f-k analysis based on Fourier transform leads to f-k panels that are dominated by sidelobes. An algorithm that uses a Bayesian approach to design an artifacts-reduced Fourier transform has been developed to overcome this shortcoming. A by-product of the method is a high-resolution periodogram. This extrapolation gives the periodogram that would have been recorded with a longer array of receivers if the data were a limited superposition of monochromatic planes waves.The technique is useful in array processing for two reasons. First, it provides spatial extrapolation of the array (subject to the above data assumption) and second, missing receivers within and outside the aperture are treated as unknowns rather than as zeros.The performance of the technique is illustrated with synthetic examples for both broad-band and narrowband data. Finally, the applicability of the procedure is assessed analyzing the f-k spectral signature of a vertical seismic profile (VSP).

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“…Numerous approaches have been proposed for noise attenuation. They include, but are not limited to, moving averaging, FK filtering, FX deconvolution (Marfurt 2006), Radon transform (Sacchi and Porsani, 1999;Sacchi et al, 2004), edge preserving smoothing (Luo, et al, 2002), scalar median filters (Biondi and Kostov, 1998;Schuster, 2005).…”

Section: Introductionmentioning

confidence: 99%