Extracting horizon patches and geo‐bodies from 3D seismic waveform sequences

Borgos, Hilde Grude; Gramstad, Oddgeir; Dahl, Geir; Guern, Pierre Le; Sønneland, L.; Rosalba, J. F.

doi:10.1190/1.2369825

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Even though these tools become better and faster there are situations where the information a geologist uses is nonlocal -a certain feature cannot be selected based on one (or more) attributes at a certain seismic reflector. To overcome these difficulties methods like sequence classification, Borgos (2006) and textures identification Carrillat (2002) have been used for non-local feature identification. In the case of features only detectable through the combination of different attributes volume co-rendering and sculpting can be used with success.…”

Section: Introductionmentioning

confidence: 99%

Seismic DNA – A novel visually guided search method using non-local search and multi-attribute data sets

Bakke

Gramstad

Sønneland

2012

SEG Technical Program Expanded Abstracts 2012

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We present a new approach to extract features from seismic data using a non-local and multi-attribute search method. By transforming seismic reflection data and attributes derived from seismic data into characters we can search for complex features in the seismic data volumes using a textbased search technology. The method presented here allows for feature patterns with a varying vertical extent, varying attribute values and a varying number of sub-features in the feature signature. We describe the method and give two examples of how it can be used. The first one shows how the number of sub-features can vary in a feature. The second one shows how we can combine different attributes in one search.

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Section: Introductionmentioning

confidence: 99%

Seismic DNA – A novel visually guided search method using non-local search and multi-attribute data sets

Bakke

Gramstad

Sønneland

2012

SEG Technical Program Expanded Abstracts 2012

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“…Horizon identification methods that do not rely on growing, such as performing clustering of the voxels based on their local neighborhood [4], result in segmentation masks that are not directly transferable to polygonal surfaces. Incremental growing methods are better suited as they can be made topology aware.…”

Section: Horizon Extraction and Representationmentioning

confidence: 99%

“…There are completely automatic approaches for horizon interpretation [4]. In their work, voxels are mapped to points in an ndimensional space based on their local waveforms.…”

Section: Related Workmentioning

confidence: 99%

Seismic volume visualization for horizon extraction

Patel

Brückner

Viola

et al. 2010

2010 IEEE Pacific Visualization Symposium (PacificVis)

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Seismic horizons indicate change in rock properties and are central in geoscience interpretation. Traditional interpretation systems involve time consuming and repetitive manual volumetric seeding for horizon growing. We present a novel system for rapidly interpreting and visualizing seismic volumetric data. First we extract horizon surface-parts by preprocessing the seismic data. Then during interaction the user can assemble in realtime the horizon parts into horizons. Traditional interpretation systems use gradient-based illumination models in the rendering of the seismic volume and polygon rendering of horizon surfaces. We employ realtime gradientfree forward-scattering in the rendering of seismic volumes yielding results similar to high-quality global illumination. We use an implicit surface representation of horizons allowing for a seamless integration of horizon rendering and volume rendering. We present a collection of novel techniques constituting an interpretation and visualization system highly tailored to seismic data interpretation.

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