Dispersion estimation from linear array data in the time-frequency plane

Roueff, Antoine; Mars, Jérôme; Chanussot, Jocelyn; Pedersen, Helle A.

doi:10.1109/tsp.2005.855430

Cited by 15 publications

(13 citation statements)

References 17 publications

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“…Note that our method differs from the method in [4] in three major respects. First instead of searching over all the parameters we estimate the phase and attenuation and search only over group slowness.…”

Section: Iv-c Overview Of Semblance Analysismentioning

confidence: 96%

“…Second our method employs semblance as a criterion to estimate the parameters. In contrast to the energy based approach of [4], this makes it feasible to extract weak modes. Finally we explicitly deal with and estimate attenuation, which is an important parameter of interest in oilfield applications.…”

Section: Iv-c Overview Of Semblance Analysismentioning

confidence: 99%

“…Recently there has been interest in the application of time frequency methods to this problem, see [4] and references therein. The approach presented here is also based on time-frequency representations but differs from the approach in [4] as discussed later.…”

Section: Introductionmentioning

confidence: 99%

“…The approach presented here is also based on time-frequency representations but differs from the approach in [4] as discussed later. First we begin by outlining the problem set-up in the next section.…”

Section: Introductionmentioning

confidence: 99%

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Automatic dispersion extraction using continuous wavelet transform

Aeron

Bose

Valero

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper we present a novel framework for automatic extraction of dispersion characteristics from acoustic array data. Traditionally high resolution narrow-band array processing techniques such as Prony's polynomial method and forward backward matrix pencil method have been applied to this problem. Fundamentally these techniques extract the dispersion components frequency by frequency in the wavenumberfrequency transform domain of the array data. The dispersion curves are subsequently extracted by a supervised post processing and labelling of the extracted wavenumber estimates, making such an approach unsuitable for automated processing. Moreover, this frequency domain processing fails to exploit useful time information. In this paper we present a method that addresses both these issues. It consists in taking the continuous wavelet transform (CWT) of the array data and then applying a wide-band array processing technique based on a modified Radon transform on the resulting coefficients to extract the dispersion curve(s). The time information retained in the CWT domain is useful not only for separating the components present but also for extracting group slowness estimates. The latter help in the automated extraction of smooth dispersion curves. In this paper we will introduce this new method referred to as the Exponential Projected Radon Transform (EPRT) in the CWT domain and limit ourselves to the analysis for the case of one dispersive mode. We will apply the method to synthetic and real data sets and compare the performance with existing methods.

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Section: Iv-c Overview Of Semblance Analysismentioning

confidence: 96%

Section: Iv-c Overview Of Semblance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic dispersion extraction using continuous wavelet transform

Aeron

Bose

Valero

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The accuracy of this characterization is highly dependent on the priori information of k(ω). As described in [20] [21], k(ω) may be available or can be derived based on physical dimensions and mechanical properties of the medium. Such pre-calibration imposes strong assumptions on the material properties and any variation in k(ω) due to, for example, caused by a change in environment [9], will degrade the localization performance.…”

Section: Time-frequency Warpingmentioning

confidence: 99%

Source localization on solids utilizing time-frequency analysis of parameterized warped signals

Kattukandy

Reju

Khong

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a new approach for source localization on solids with applications to human-computer interface. We analyze the wave propagation of flexural modes of vibration, generated by an impact on a solid surface, to characterize the dispersive linear time-varying system having non-linear phase response. We show that a difference in dispersion between two signals propagating through solids can be mapped directly to the relative propagation distance if the signals are appropriately time-warped. We then exploit this important property for source localization by computing the similarity of the warped signals in the time-frequency domain. As the proposed source localization algorithm jointly estimates warping-based polynomial parameters and source location, the method does not require pre-calibration.Index Terms-Human-computer interaction, frequency dispersion, source localization on solids, unitary warping.

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