Improved fault segmentation using a dip guided and modified 3D Sobel filter

Aqrawi, A.A.; Boe, Trond Hellem

doi:10.1190/1.3628241

Cited by 90 publications

(27 citation statements)

References 10 publications

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“…When used to highlight faults, some sort of averaging or smoothing of semblance (or some other attribute) is required, as emphasized by Gersztenkorn and Marfurt (1999) and Aqrawi and Boe (2011). These authors describe vertical smoothing of fault attributes.…”

Section: Fault Imagesmentioning

confidence: 96%

Fault surfaces and fault throws from 3D seismic images

Hale

2012

SEG Technical Program Expanded Abstracts 2012

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A new method for processing 3D seismic images yields images of fault likelihoods and corresponding fault strikes and dips. A second process automatically extracts from those images fault surfaces represented by meshes of quadrilaterals. A third process uses differences between seismic image sample values alongside those fault surfaces to automatically estimate fault throw vectors. While some of the faults found in one 3D seismic image have an unusual conical shape, displays of unfaulted images illustrate the fidelity of the estimated fault surfaces and fault throw vectors.

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Section: Fault Imagesmentioning

confidence: 96%

Fault surfaces and fault throws from 3D seismic images

Hale

2012

SEG Technical Program Expanded Abstracts 2012

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“…Several other studies are supported by image processing theory to identify faults and channels. In this approach, edge detection procedures and digital filters are commonly used (Aqrawi andBoe, 2011, Jing et al, 2007). In a recent work, Pampanelli et al (2013) took advantage of the strong mathematical background inherent to these approaches and used the first-order directional derivative to enhance discontinuities along horizons.…”

Section: Introductionmentioning

confidence: 99%

Minimal similarity accumulation attribute for fault enhancement

Figueiredo

Faustino

Peçanha

et al. 2014

SEG Technical Program Expanded Abstracts 2014

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In this work we present a new method for seismic fault enhancement on volumetric grids called Minimal Similarity Accumulation (MSA). The seismic reflection data is transformed into a multi-dimensional amplitude space and a clustering procedure is applied in order to minimize the noise interference. Thereafter, we use an algorithm to compute the MSA for each voxel in the volume. This is done by applying an autoadaptable function that uses the voxel neighboring information. The MSA measurement globally highlights the fault regions presented in the original volume. To validate the proposed method we use the volume of the Netherlands offshore F3 block downloaded from the Open Seismic Repository. In order to assess the proposed method and illustrate it advantages, a set of vertical 2D slices of the seismic data are presented providing a comparison between our results and images manually interpreted by a geologist. Finally, we conclude that the proposed method is sufficiently accurate.

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“…Faults, caused by the movement of rocks, represent discontinuity along horizons. To characterize the discontinuity, researchers have introduced various seismic attributes such as semblance [1], variance [2], curvature [3], and gradient amplitude [4] [5]. In addition to these basic attribute-based approaches, a number of studies have proposed complex methods involving image processing techniques to semi-automatically detect faults.…”

Section: Introductionmentioning

confidence: 99%

Fault detection using color blending and color transformations

Wang

Temel

AlRegib

2014

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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In the field of seismic interpretation, univariate databased maps are commonly used by interpreters, especially for fault detection. In these maps, the contrast between target regions and the background is one of the main factors that affect the accuracy of interpretation. Since univariate data-based maps are not capable of providing a high-contrast representation, to overcome this issue, we turn them into multivariate data-based representations using color blending. We blend neighboring time sections or frames that are viewed in the time direction of migrated seismic volumes as if they corresponded to the red, green, and blue channels of a color image. Furthermore, to extract more reliable structural information, we apply color transformations. Experimental results show that the proposed method improves the accuracy of fault detection by limiting the average distance between detected fault lines and the ground truth into one pixel.

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Improved fault segmentation using a dip guided and modified 3D Sobel filter

Cited by 90 publications

References 10 publications

Fault surfaces and fault throws from 3D seismic images

Fault surfaces and fault throws from 3D seismic images

Minimal similarity accumulation attribute for fault enhancement

Fault detection using color blending and color transformations

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