Noise attenuation in multimeasurement streamer data using weighted vector auto regressive operators

Kamil, Y.I.; Vermeer, P. L.; Vassallo, Massimiliano

doi:10.1190/segam2015-5876722.1

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Furthermore, one can attenuate random noise in the case of multicomponent measurements via vector‐autoregressive (VAR) models (Naghizadeh and Sacchi ; Kamil et al . ). The development of autoregressive models for quaternion signals (Q‐AR) (Ginzberg and Walden ) introduces another opportunity in the treatment of vector‐valued signals.…”

Section: Theorymentioning

confidence: 97%

“…This same technique can also be implemented in three dimensions using two-dimensional convolutions under the assumption of planar events in time (Chase 1992). Furthermore, one can attenuate random noise in the case of multicomponent measurements via vector-autoregressive (VAR) models (Naghizadeh and Sacchi 2012;Kamil et al 2015). The development of autoregressive models for quaternion signals (Q-AR) (Ginzberg and Walden 2013) introduces another opportunity in the treatment of vector-valued signals.…”

Section: T H E O R Y F − X Prediction Via Autoregressive Modelsmentioning

confidence: 99%

“…The FXDECON is a complex Wiener filtering technique that estimates the predictable portions of a given signal embedded in random noise via autoregressive (AR) models. Naghizadeh and Sacchi () and Kamil, Vermeer and Vassallo () show how one can extend the FXDECON to the case of multicomponent data through vector‐autoregressive (VAR) models. Both publications indicate better random noise suppression when compared with its scalar version due to the additional information exploited by VAR models.…”

Section: Introductionmentioning

confidence: 99%

“…The FXDECON is a complex Wiener filtering technique that estimates the predictable portions of a given signal embedded in random noise via autoregressive (AR) models. Naghizadeh and Sacchi (2012) and Kamil, Vermeer and Vassallo (2015) show how one can extend the FXDECON to the * E-mail: figueire@ualberta.ca † Equally contributing authors. case of multicomponent data through vector-autoregressive (VAR) models.…”

Section: Introductionmentioning

confidence: 99%

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Widely linear denoising of multicomponent seismic data

Bahia

Sacchi

2019

Geophysical Prospecting

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Seismic data processing is a challenging task, especially when dealing with vector‐valued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components. Mitigating such noise while preserving the signal of interest is a primary goal in the seismic‐processing workflow. The frequency‐space deconvolution is a well‐known linear prediction technique, which is commonly used for random noise suppression. This paper represents vector‐field seismic data through quaternion arrays and shows how to mitigate random noise by proposing the extension of the frequency‐space deconvolution to its hypercomplex version, the quaternion frequency‐space deconvolution. It also shows how a widely linear prediction model exploits the correlation between data components of improper signals. The widely linear scheme, named widely‐linear quaternion frequency‐space deconvolution, produces longer prediction filters, which have enhanced signal preservation capabilities shown through synthetic and field vector‐valued data examples.

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Section: Theorymentioning

confidence: 97%

Section: T H E O R Y F − X Prediction Via Autoregressive Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The FXDECON is a complex Wiener filtering technique that estimates the predictable portions of a given signal embedded in random noise via autoregressive (AR) models. Naghizadeh and Sacchi (2012) and Kamil, Vermeer and Vassallo (2015) show how one can extend the FXDECON to the * E-mail: figueire@ualberta.ca † Equally contributing authors. case of multicomponent data through vector-autoregressive (VAR) models.…”

Section: Introductionmentioning

confidence: 99%

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Widely linear denoising of multicomponent seismic data

Bahia

Sacchi

2019

Geophysical Prospecting

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Quaternion-based sparse tight frame for multicomponent signal recovery

Zhao

Yasin

et al. 2020

GEOPHYSICS

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Multicomponent noise attenuation often presents more severe processing challenges than scalar data owing to the uncorrelated random noise in each component. Meanwhile, weak signals merged in the noise are easier to degrade using the scalar processing workflows while ignoring their possible supplement from other components. For seismic data preprocessing, transform-based approaches have achieved improved performance on mitigating noise while preserving the signal of interest, especially when using an adaptive basis trained by dictionary-learning methods. We have developed a quaternion-based sparse tight frame (QSTF) with the help of quaternion matrix and tight frame analyses, which can be used to process the vector-valued multicomponent data by following a vectorial processing workflow. The QSTF is conveniently trained through iterative sparsity-based regularization and quaternion singular-value decomposition. In the quaternion-based sparse domain, multicomponent signals are orthogonally represented, which preserve the nonlinear relationships among multicomponent data to a greater extent as compared with the scalar approaches. We test the performance of our method on synthetic and field multicomponent data, in which component-wise, concatenated, and long-vector models of multicomponent data are used as comparisons. Our results indicate that more features, specifically the weak signals merged in the noise, are better recovered using our method than others.

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Noise types and their attenuation in towed marine seismic: A tutorial

Hlebnikov

Elboth²,

Vinje³

et al. 2021

GEOPHYSICS

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The presence of noise in towed marine seismic data is a long-standing problem. The various types of noise present in marine seismic records are never truly random. Instead, seismic noise is more complex and often challenging to attenuate in seismic data processing. Therefore, we examine a wide range of real data examples contaminated by different types of noise including swell noise, seismic interference noise, strumming noise, passing vessel noise, vertical particle velocity noise, streamer hit and fishing gear noise, snapping shrimp noise, spike-like noise, cross-feed noise and streamer mounted devices noise. The noise examples investigated focus only on data acquired with analogue group-forming. Each noise type is classified based on its origin, coherency and frequency content. We then demonstrate how the noise component can be effectively attenuated through industry standard seismic processing techniques. In this tutorial, we avoid presenting the finest details of either the physics of the different types of noise themselves or the noise attenuation algorithms applied. Rather, we focus on presenting the noise problems themselves and show how well the community is able to address such noise. Our aim is that based on the provided insights, the geophysical community will be able to gain an appreciation of some of the most common types of noise encountered in marine towed seismic, in the hope to inspire more researchers to focus their attention on noise problems with greater potential industry impact.

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Noise attenuation in multimeasurement streamer data using weighted vector auto regressive operators

Cited by 5 publications

References 5 publications

Widely linear denoising of multicomponent seismic data

Widely linear denoising of multicomponent seismic data

Quaternion-based sparse tight frame for multicomponent signal recovery

Noise types and their attenuation in towed marine seismic: A tutorial

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