Retrieval and suppression of surface waves using interferometry by correlation and by deconvolution

Vasconcelos, Ivan; Gaiser, James; Calvert, Alex; Calderón‐Macías, Carlos

doi:10.1190/1.3063876

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…by changing the integration interval ∂V in Equation 1 to ∂V sur f ace which is a subset of ∂V containing sources contribute mainly to surface wave responses. A more detailed explanation has been showed by Vasconcelos et al (2008). Dong et al (2006) also showed that even if integrating over all the sources according to Equation 1, the surface waves have been relatively amplified compared to the reflections in the full response G(r A , r B ).…”

Section: Prediction Of Groundrollmentioning

confidence: 92%

“…In other words, we need to match our groundroll prediction to the actual groundroll in the data before we subtract it. Vasconcelos et al (2008) showed two ways to adaptively subtract the correlationgroundroll prediction by using the least-squares subtraction technique. One is to subtract the prediction from the original full data, the other is to subtract from the pseudo full-wavefield response produced by Equation 1.…”

Section: Separation Of Groundrollmentioning

confidence: 99%

“…Similar to the removal of surface related multiples, the removal of groundroll consists of two parts: prediction and separation. For the prediction, an interferometry method has been recently proposed by Halliday et al (2007) and followed by others including Vasconcelos et al (2008); Dong et al (2006). For the second separation step, adaptive-subtraction methods, which are used during surface-related multiple elimination (SRME) (Verschuur et al, 1992), have been used successfully by Halliday et al (2007); Vasconcelos et al (2008); Dong et al (2006).…”

Section: Introductionmentioning

confidence: 99%

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Groundroll prediction by interferometry and separation by curvelet‐domain matched filtering

Yan¹,

Herrmann²

2009

SEG Technical Program Expanded Abstracts 2009

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SUMMARYThe removal of groundroll in land based seismic data is a critical step for seismic imaging. In this paper, we introduce a work flow to predict the groundroll by interferometry and then separate the groundroll in the curvelet domain. Thus workflow is similar to the workflow of surface-related multiple elimination (SRME). By exploiting the adaptability and sparsity of curvelets, we are able to significantly improve the separation of groundroll in comparison to results yielded by frequencydomain adaptive subtraction methods. We provide synthetic data example to illustrate our claim.

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Section: Prediction Of Groundrollmentioning

confidence: 92%

Section: Separation Of Groundrollmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Groundroll prediction by interferometry and separation by curvelet‐domain matched filtering

Yan¹,

Herrmann²

2009

SEG Technical Program Expanded Abstracts 2009

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Interferometric ground-roll removal: Attenuation of scattered surface waves in single-sensor data

et al. 2010

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Land seismic data are contaminated by surface waves ͑or ground roll͒. These surface waves are a form of source-generated noise and can be strongly scattered by near-surface heterogeneities. The resulting scattered ground roll can be particularly difficult to separate from the desired reflection data, especially when this scattered ground roll propagates in the crossline direction. We have used seismic interferometry to estimate scattered surface waves, recorded during an exploration seismic survey, between pairs of receiver locations. Where sources and receivers coincide, these interreceiver surface-wave estimates were adaptively subtracted from the data. This predictive-subtraction process can successfully attenuate scattered surface waves while preserving the valuable reflected arrivals, forming a new method of scattered ground-roll attenuation. We refer to this as interferometric ground-roll removal.

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Underestimation of body waves and feasibility of surface-wave reconstruction by seismic interferometry

Forghani

Snieder

2010

The Leading Edge

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Interferometry is an approach to extract the Green's function between two receivers as if the virtual source is at one of the receiver locations. The Green's function which is the response to an impulsive source, accounts for the wave propagation in the medium between the two receivers. It is commonly thought that Green's function estimates obtained from interferometry accurately represent the full Green's function, but this is not always the case. In fact, the accuracy of the retrieved Green's function, which consists of the surface and body waves, is in practice restricted by the limited distribution of the sources of field fluctuations. We demonstrate the importance of adequate source distribution for the accuracy of the retrieved Green's function, using examples from interferometry applications. In these examples, sources are controlled (as in exploration seismology) or uncontrolled (as in crustal seismology).

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Retrieval and suppression of surface waves using interferometry by correlation and by deconvolution

Cited by 7 publications

References 5 publications

Groundroll prediction by interferometry and separation by curvelet‐domain matched filtering

Groundroll prediction by interferometry and separation by curvelet‐domain matched filtering

Interferometric ground-roll removal: Attenuation of scattered surface waves in single-sensor data

Underestimation of body waves and feasibility of surface-wave reconstruction by seismic interferometry

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