L1 pseudo-Vz estimation and deghosting of single-component marine towed-streamer data

Ferber, Ralf; Caprioli, Philippe; West, Lee

doi:10.1190/ist092012-001.16

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…After source side deghosting, the data can now be treated similarly for receiver side deghosting, albeit in the in-line tau-p domain to allow for the (inline) dip variation of the ghost wavelet. Momoh et al (2015) already applied the pseudo-Vz receiver side deghosting technique (Ferber et al, 2013), which is closely related to sparsityenhanced deconvolution of the receiver ghost wavelet in the tau-p domain, to the data (without any attempt at source side deghosting). We applied the above-mentioned pseudo-Vz receiver deghosting technique to the data after source side deghosting and show the results in Fig.…”

Section: Figure 15mentioning

confidence: 99%

Sparsity‐enhanced wavelet deconvolution

Ferber

Momoh

2018

Geophysical Prospecting

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We propose a three‐step bandwidth enhancing wavelet deconvolution process, combining linear inverse filtering and non‐linear reflectivity construction based on a sparseness assumption. The first step is conventional Wiener deconvolution. The second step consists of further spectral whitening outside the spectral bandwidth of the residual wavelet after Wiener deconvolution, i.e., the wavelet resulting from application of the Wiener deconvolution filter to the original wavelet, which usually is not a perfect spike due to band limitations of the original wavelet. We specifically propose a zero‐phase filtered sparse‐spike deconvolution as the second step to recover the reflectivity dominantly outside of the bandwidth of the residual wavelet after Wiener deconvolution. The filter applied to the sparse‐spike deconvolution result is proportional to the deviation of the amplitude spectrum of the residual wavelet from unity, i.e., it is of higher amplitude; the closer the amplitude spectrum of the residual wavelet is to zero, but of very low amplitude, the closer it is to unity. The third step consists of summation of the data from the two first steps, basically adding gradually the contribution from the sparse‐spike deconvolution result at those frequencies at which the residual wavelet after Wiener deconvolution has small amplitudes. We propose to call this technique “sparsity‐enhanced wavelet deconvolution”. We demonstrate the technique on real data with the deconvolution of the (normal‐incidence) source side sea‐surface ghost of marine towed streamer data. We also present the extension of the proposed technique to time‐varying wavelet deconvolution.

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Section: Figure 15mentioning

confidence: 99%

Sparsity‐enhanced wavelet deconvolution

Ferber

Momoh

2018

Geophysical Prospecting

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“…Deghosting is a critical part of the broadband processing workflow, and a lot of effort recently has been put into developing robust algorithms for this purpose (Ferber, Caprioli and West 2012;Wang and Peng 2012;Rickett et al 2014). The goal of this paper is not to deghost data per se but to answer the question: what can we say about the likely values of the ghost delay time given only the geometry of the problem?…”

Section: Introductionmentioning

confidence: 99%

A probabilistic model for ghost delay time estimation based on recording geometry

Cecı́lio

2017

Geophysical Prospecting

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In order to deconvolve the ghost response from marine seismic data, an estimate of the ghost operator is required. Typically, this estimate is made using a model of in‐plane propagation, i.e., the ray path at the receiver falls in the vertical plane defined by the source and receiver locations. Unfortunately, this model breaks down when the source is in a crossline position relative to the receiver spread. In this situation, in‐plane signals can only exist in a small region of the signal cone. In this paper, we use Bayes' theory to model the posterior probability distribution functions for the vertical component of the ray vector given the known source–receiver azimuth and the measured inline component of the ray vector. This provides a model for the ghost delay time based on the acquisition geometry and the dip of the wave in the plane of the streamer. The model is fairly robust with regard to the prior assumptions and controlled by a single parameter that is related to the likelihood of in‐plane propagation. The expected values of the resulting distributions are consistent with the deterministic in‐plane model when in‐plane likelihood is high but valid everywhere in the signal cone. Relaxing the in‐plane likelihood to a reasonable degree radically simplifies the shape of the expected‐value surface, lending itself for use in deghosting algorithms. The model can also be extended to other plane‐wave processing problems such as interpolation.

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“…Lacking multimeasurements for marine data, researchers have devised a number of ways to mitigate the receiver ghost problem through either specialized acquisition (Ray 1982;Dragoset 1991;Soubaras 2010) or through estimation procedures based on the either the spectral ghost model or estimation of a second measurement (Robertsson and Kragh 2002;Ferber et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Wave equation receiver deghosting: A provocative example

Beasley¹,

Coates²,

Ji³

et al. 2013

SEG Technical Program Expanded Abstracts 2013

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The problem of counteracting the effects of the receiver ghost in marine data has generally been addressed either by making complementary measurements such as hydrophone pairs or hydrophone and particle velocity sensors or by estimation of the effect through the so-called ghost model.Here, we discuss a technique based on the wave equation that accomplishes up/down wavefield separation based on a single measurement that does not rely on the spectral ghost model and does not rely on statistical or other means of estimating missing frequencies. While it is a distinct advantage to record multiple measurements, such systems are limited in availability and, moreover, significant legacy data exist that could benefit from deghosting. We briefly review the theory for wave equation receiver deghostingfirst presented by Beasley et al., (2013) and then present examples that confirm the accuracy of the approach. We illustrate a significant difference between this approach and many other approaches that use single measurements. Using the notch frequency in a mono-frequency plane wave example, we show that our approach accurately reconstructs the upgoing wavefield without benefit of information from other frequencies; a task that we expect would pose challenges to other single-measurement approaches.

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L1 pseudo-Vz estimation and deghosting of single-component marine towed-streamer data

Cited by 5 publications

References 5 publications

Sparsity‐enhanced wavelet deconvolution

Sparsity‐enhanced wavelet deconvolution

A probabilistic model for ghost delay time estimation based on recording geometry

Wave equation receiver deghosting: A provocative example

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