Structurally consistent<i>f‐x</i>filtering

Traonmilin, Yann; Herrmann, P.

doi:10.1190/1.3063891

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…The results, although using limited spatial constraints, reveal in Figure 2c a layering consistent with the well log. Some discontinuities remained, but as seen in Figure 2d applying a consistent structural filtering (Traonmilin and Herrmann, 2008) can assist in extracting the useful information.…”

Section: Data Examples First Examplementioning

confidence: 99%

3D PP‐PS joint Stratigraphic Elastic Inversion through combined AVA attributes

Garotta

Granger

2010

SEG Technical Program Expanded Abstracts 2010

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Multi-component seismic is increasingly being recognized not only as a solution to imaging problems, particularly where conventional seismic fails, but also as a tool for reservoir characterization. But geologists are often unenthusiastic to extract earth information from multi-mode seismic images because of their visual differences due to their own physical responses, and when the seismic images are equivalent the PS data does not provide any additional information by itself. The stratigraphic inversion is considered as a tool to transform seismic amplitude variations into petrophysical variations. With multicomponent acquisition, the AVA attributes of PP and PS modes are usually accessible, providing the additional shear wave information for a more constrained elastic inversion. Our inversion method simultaneously reconciles amplitude and time information from both PP and PS-wave data by equating the value of γ = V p /V s derived from the velocities or transit time ratio (γ t) and the value derived from the reflectivity ratio (γ a), providing not only the high-resolution of the V p /V s = γ r or the Poison ratio but also eventually V p , V s and densities (Garotta et al., 2000). A trace by trace inversion using simulated annealing process was described by (Dariu et al., 2003). In this paper we propose to examine some new possibilities and an extension of the method to 3D constraints in order to decrease the sensitivity to the noise and take into account the geological structure.

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Section: Data Examples First Examplementioning

confidence: 99%

3D PP‐PS joint Stratigraphic Elastic Inversion through combined AVA attributes

Garotta

Granger

2010

SEG Technical Program Expanded Abstracts 2010

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“…We now have a wide variety of tools to perform such a task. From prediction and projection filtering (Canales 1984; Soubaras 1995) to coherency enhancement (Gulunay 2007; Traonmilin and Herrmann 2008), we can efficiently address many situations where random noise contaminates data. These methods rely on the hypothesis that the signal is spatially coherent in order to estimate the signal.…”

Section: Introductionmentioning

confidence: 99%

Statics‐preserving projection filtering

Traonmilin

Gülünay²

2012

Geophysical Prospecting

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A B S T R A C TProjection filtering has been used for many years in seismic processing as a tool to extract a signal out of noisy data. The effectiveness of projection filtering reaches a limit when seismic events are affected by static shifts. Such shifts degrade the lateral coherency of the data, which is the strongest assumption made by projection filtering. We propose an algorithm to estimate projection filters and static shifts simultaneously in order to perform noise attenuation in the presence of static shifts in the data. We then show results on synthetic and real data to demonstrate the denoising capabilities of our algorithm.

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“…AlBinHassan, Luo and Al-Faraj (2006) developed 3D edge-preserving smoothing operators to reduce noise without blurring sharp discontinuities. Traonmilin and Herrmann (2008) used a mix of finite-impulse-response (FIR) and infinite-impulse-response filters, followed by f -x * This paper is based on the extended abstract V026 presented at the 71th EAGE Conference & Exhibition Incorporating SPE EUROPEC 2009, 8-11 June 2009 in Amsterdam, the Netherlands. †E-mail: yangliu1979@jlu.edu.cn filtering, to perform structure-preserving seismic processing.…”

Section: Introductionmentioning

confidence: 99%

“…AlBinHassan, Luo and Al‐Faraj (2006) developed 3D edge‐preserving smoothing operators to reduce noise without blurring sharp discontinuities. Traonmilin and Herrmann (2008) used a mix of finite‐impulse‐response (FIR) and infinite‐impulse‐response filters, followed by f ‐ x filtering, to perform structure‐preserving seismic processing. Whitcombe et al (2008) introduced a frequency‐dependent, structurally conformable filter.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear structure‐enhancing filtering using plane‐wave prediction*

Liu

Fomel

Liu

2010

Geophysical Prospecting

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A B S T R A C TAttenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation. We present a new technique, which aims at reducing random noise while protecting structural information. The technique is based on combining structure prediction with either similarity-mean filtering or lower-upper-middle filtering. We use structure prediction to form a structural prediction of seismic traces from neighbouring traces. We apply a non-linear similarity-mean filter or an lower-upper-middle filter to select best samples from different predictions. In comparison with other common filters, such as mean or median, the additional parameters of the non-linear filters allow us to better control the balance between eliminating random noise and protecting structural information. Numerical tests using synthetic and field data show the effectiveness of the proposed structure-enhancing filters. I N T R O D U C T I O NExtracting structural information is the most important goal of seismic interpretation. A number of approaches have been proposed to preserve and enhance structural information. Hoeber, Brandwood and Whitcombe (2006) applied nonlinear filters, such as median, trimmed mean and adaptive Gaussian, over planar surfaces parallel to the structural dip. Hale (2009) and Fehmers and Höcker (2003) applied structure-oriented filtering based on anisotropic diffusion. Fomel and Guitton (2006) suggested the method of planewave construction. AlBinHassan, Luo and Al-Faraj (2006) developed 3D edge-preserving smoothing operators to reduce noise without blurring sharp discontinuities. Traonmilin and Herrmann (2008) used a mix of finite-impulse-response (FIR) and infinite-impulse-response filters, followed by f -x *

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Structurally consistentf‐xfiltering

Cited by 9 publications

References 4 publications

3D PP‐PS joint Stratigraphic Elastic Inversion through combined AVA attributes

3D PP‐PS joint Stratigraphic Elastic Inversion through combined AVA attributes

Statics‐preserving projection filtering

Nonlinear structure‐enhancing filtering using plane‐wave prediction*

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