Coherent noise attenuation in the radial trace domain

Henley, David C.

doi:10.1190/1.1598134

Cited by 106 publications

(30 citation statements)

References 4 publications

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“…The linear noise (surface waves, first arrivals, ...etc.) was attenuated using radial trace filtering (Henley, 2003) targeting specific dips with estimated velocity trends and a specific time origin. This is a cascaded process where linear noise trends are estimated first, then subtracted from the data through a least-squares method.…”

Section: Pre-processing Stepsmentioning

confidence: 99%

Surface-consistent matching filters for time-lapse processing

Almutlaq

Margrave

2012

SEG Technical Program Expanded Abstracts 2012

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The problem of mismatch between repeated time-lapse seismic surveys remains a challenge, particularly for land acquisition. In this dissertation, we present a new algorithm, which is an extension of the surface-consistent model, and which minimizes the mismatch between surveys, hence improving repeatability.We introduce the concept of surface-consistent matching filters (SCMF) for processing time-lapse seismic data, where matching filters are convolutional filters that minimize the sum-squared error between two signals. Since in the Fourier domain, a matching filter is the spectral ratio of the two signals, we extend the well known surface-consistent hypothesis such that the data term is a trace-by-trace spectral ratio of two datasets instead of only one (i.e. surface-consistent deconvolution). To avoid unstable division of spectra, we compute the spectral ratios in the time domain by first designing tracesequential, least-squares matching filters, then Fourier transforming them. A subsequent least-squares solution then factors the trace-sequential matching filters into four operators: two surface-consistent (source and receiver), and two subsurface-consistent (offset and midpoint).We apply the algorithm to two datasets: a synthetic time-lapse model and field data from a CO 2 monitoring site in Northern Alberta. In addition, two common time-lapse processing schemes (independent processing and simultaneous processing) are compared.We present a modification of the simultaneous processing scheme as a direct result of applying the new SCMF algorithm. The results of applying the SCMF together with the new modified simultaneous processing flow reveal the potential benefit of the method, however some challenges remain, specifically in the presence of random noise.i

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Section: Pre-processing Stepsmentioning

confidence: 99%

Surface-consistent matching filters for time-lapse processing

Almutlaq

Margrave

2012

SEG Technical Program Expanded Abstracts 2012

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“…To accomplish this, linear noise over the shallow part of the section was attenuated by applying a radial filter. One of the benefits of using the radial filter, designed by Dave Henley, is its ability to remove linear noise at various velocities, whether originating from one source or from several sources (Henley, 2003). After testing the radial filter, there do not appear to be events under the 8-12 Hz low pass bandwidth.…”

Section: Radial Filtermentioning

confidence: 99%

“…Various noise attenuation and geometrical spreading methods were investigated, including TAR (true amplitude recovery), surface-wave attenuation, surface consistent amplitudes and radial filter. However, the radial filter was the only method that did not remove near-surface events or create a 'shadow zone' below the first breaks (Henley, 2003). Since the target reservoir is less than 500 m in depth the radial filter was ideal for removing low-frequency shotgenerated linear noise.…”

Section: Radial Filtermentioning

confidence: 99%

14. Multicomponent Processing of Seismic Data at the Jackfish Heavy-Oil Project, Alberta

Pengelly¹,

Lines²,

Lawton³

2010

Heavy Oils

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This investigation was undertaken to evaluate the processing flows needed to obtain vertical and radial post-stack migrated seismic sections from a heavy oil reservoir in Eastern Alberta. The radial filter was found to successfully attenuate shot-generated linear noise on both the vertical and radial components. Gabor deconvolution was found to successfully boost signal to noise in low fold areas of the data. The depth-variant stack has shown to have greater frequency in the near surface data compared to the CCP stack for the radial component.

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“…It has the significant characteristics of relatively low velocity, low frequency, high amplitude and strong energy (Sheriff, 2002). Because of its dispersive nature and low velocity, ground-roll masks the shallow reflections at near offsets and deep reflections at far offsets (Saatcilar & Canitez, 1988;McMechan & Sun, 1991;Saatcilar & Canitez, 1994;Henley, 2003) and also distorts reflection events by interfering with them.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Signal Decomposition and Filtering Using Binomial Filters Applied to the Ground-Roll Attenuation

Ariza¹,

Porsani²

2016

Rev. Bras. Geof.

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ABSTRACT. The ground-roll is a type of noise normally present in land seismic data. It strongly harms the signal-to-noise ratio, and interferes in several stages of the seismic data processing, strongly affecting the final quality of the obtained seismic images. For the attenuation of the ground-roll we propose an adaptive filtering method that uses binomials filters obtained from the convolution of pairs of dipoles (1, c) and (c, −1). The coefficient c corresponds to the first coefficient of the prediction error operator, obtained through the Burg algorithm. This filtering method allows for the decomposition of signals in frequency bands, from the lowest frequency band to the highest band. The method is applied in adaptive form, in sliding windows of time, adapting themselves to the variations in the frequency content of the data. Its implementation and use in seismic data processing is relatively simple and computationally efficient.Keywords: seismic noise, signal-to-noise ratio, adaptive filters, Burg algorithm, seismic signal decomposition. RESUMO.O ground-rollé um tipo de ruído normalmente presente nos dados sísmicos terrestres. Ele prejudica muito a razão sinal-ruído e interfere em vários estágios do processamento de dados sísmicos, afetando fortemente a qualidade final das imagens sísmicas obtidas. Para atenuação desse ruído, propomos um método de filtragem adaptativa que utiliza filtros binomiais construídos a partir da convolução de pares de dipolos (1, c) e (c, −1). O coeficiente c corresponde ao primeiro coeficiente do operador de erro de predição eé obtido através do algoritmo de Burg. Este método de filtragem adaptativa permite a decomposição de sinais em bandas de frequência, desde a banda de mais baixa frequência até a de mais alta. O métodoé aplicado na forma adaptativa, utilizando janelas deslizantes de tempo, adaptando-seàs variações do conteúdo de frequência dos dados ao longo do registro sísmico. Sua implementação e uso, no processamento de dados sísmicos,é relativamente simples e computacionalmente eficiente.Palavras-chave: ruídos sísmicos, relação sinal-ruído, filtragem adaptativa, algoritmo de Burg, decomposição do sinal sísmico.

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Coherent noise attenuation in the radial trace domain

Cited by 106 publications

References 4 publications

Surface-consistent matching filters for time-lapse processing

Surface-consistent matching filters for time-lapse processing

14. Multicomponent Processing of Seismic Data at the Jackfish Heavy-Oil Project, Alberta

Adaptive Signal Decomposition and Filtering Using Binomial Filters Applied to the Ground-Roll Attenuation

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