Dealing with the noise — Improving seismic whitening and seismic inversion workflows using frequency split structurally oriented filters

Helmore, Steven

doi:10.1190/1.3255559

Cited by 17 publications

(7 citation statements)

References 4 publications

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“…If the highest frequency coherence is lower than the others, there will be less blue in the image, resulting in yellow. In contrast, if the lowest frequency coherence image is lower, there will be less Helmore's (2009) approach, (e) spectral balancing run on data in (a) using thin-bed reflectivity inversion, (f) spectral balancing using thin-bed reflectivity inversion run on data in (a) subsampled to half the original sample rate, (g) frequency bandwidth extension of data in (a) using thin-bed reflectivity inversion, and (h) AVT run on the data in (a). The top row shows broadband coherence, whereas the bottom row shows multispectral coherence computed from six voice components (data courtesy of TGS, Houston).…”

Section: Rgb Color Blendingmentioning

confidence: 98%

Coherence attribute applications on seismic data in various guises — Part 1

Chopra

Marfurt

2018

Interpretation

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We have previously discussed some alternative means of modifying the frequency spectrum of the input seismic data to modify the resulting coherence image. The simplest method was to increase the high-frequency content by computing the first and second derivatives of the original seismic amplitudes. We also evaluated more sophisticated techniques, including the application of structure-oriented filtering to different spectral components before spectral balancing, thin-bed reflectivity inversion, bandwidth extension, and the amplitude volume technique. We further examine the value of coherence computed from individual spectral voice components, and alternative means of combining three or more such coherence images, providing a single volume for interpretation.

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Section: Rgb Color Blendingmentioning

confidence: 98%

Coherence attribute applications on seismic data in various guises — Part 1

Chopra

Marfurt

2018

Interpretation

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“…Th is procedure reduces noise in selected frequency bands and results in higher signal-to-noise ratio as well as enhanced resolution. Structurally oriented fi lters do not suff er from windowing artifacts and are precisely adapted to the local dip (Helmore, 2009).…”

Section: Alternative Noise-suppression Workfl Owsmentioning

confidence: 99%

Coherence and curvature attributes on preconditioned seismic data

2011

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Seismic data are usually contaminated with both random and coherent noise, even when the data have been properly migrated and are multiple-free. Seismic attributes are particularly effective at extracting subtle features from relatively noise-free data. Certain types of noise can be addressed by the interpreter through careful structure-oriented filtering or postmigration footprint suppression. However, if the data are contaminated by multiples or are poorly focused and imaged due to inaccurate velocities, the data need to go back to the processing team.

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“…Frequency split structurally oriented filtering: Helmore (2009) introduced frequency split structurally oriented filtering (Figure 1) wherein the input seismic data is divided into a number of frequency bands, followed by running structurally oriented filters separately to each of the bands and then recombining the results. This procedure reduces noise in selected frequency bands and results in higher signal-to-noise ratio as well as enhanced resolution.…”

Section: Enhancing the Frequency Bandwidth Of Seismic Datamentioning

confidence: 99%

“…This procedure reduces noise in selected frequency bands and results in higher signal-to-noise ratio as well as enhanced resolution. Structurally oriented filters do not suffer from windowing artifacts and are precisely adapted to the local dip (Helmore, 2009).…”

Section: Enhancing the Frequency Bandwidth Of Seismic Datamentioning

confidence: 99%

Seismic attributes on frequency‐enhanced seismic data

Chopra

Marfurt

Misra

2010

SEG Technical Program Expanded Abstracts 2010

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Seismic data are usually contaminated by both random and coherent noise, even when the data have been migrated reasonably well and are multiple-free. Seismic attributes are particularly effective at extracting subtle features from relatively noise-free data. Certain types of noise can be addressed by the interpreter through careful structure-oriented filtering or post migration footprint suppression. However, if the data are contaminated by multiples or are poorly focused and imaged due to inaccurate velocities, the data need to go back to the processing team to alleviate those problems.

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Dealing with the noise — Improving seismic whitening and seismic inversion workflows using frequency split structurally oriented filters

Cited by 17 publications

References 4 publications

Coherence attribute applications on seismic data in various guises — Part 1

Coherence attribute applications on seismic data in various guises — Part 1

Coherence and curvature attributes on preconditioned seismic data

Seismic attributes on frequency‐enhanced seismic data

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