Seismic texture classification applied to processed 2D and 3D seismic data

Vinther, Rikke

doi:10.1190/1.1886112

Cited by 9 publications

(2 citation statements)

References 1 publication

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“…Texture attributes are based on statistics, making them an alternative to ordinary attribute analysis (Vinther, 1997) because they are less sensitive to seismic waveform than coherence or curvature (de Matos et al, 2011). Various methods are available for texture analysis.…”

Section: Seismic Attribute Methodologymentioning

confidence: 99%

Interpretation of fractured zones using seismic attributes — Case study from Teapot Dome, Wyoming, USA

Schneider

Eichkitz²,

Schreilechner³

et al. 2016

Interpretation

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We have used poststack seismic attributes to describe the fracture network of the naturally fractured Tensleep Formation at Teapot Dome, Wyoming, USA. The attributes include coherence, coherence based on spectral decomposed seismic data, attributes based on curvature, and textural attributes based on the gray-level co-occurrence matrix (GLCM). Results were compared with image log interpretations of four wells. Seismic attribute analysis allowed determination of strikes and dips as well as the intensity of fractures. The GLCMbased attributes proved especially valuable for building a discrete fracture network.

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Section: Seismic Attribute Methodologymentioning

confidence: 99%

Interpretation of fractured zones using seismic attributes — Case study from Teapot Dome, Wyoming, USA

Schneider

Eichkitz²,

Schreilechner³

et al. 2016

Interpretation

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“…They have proven to be successful for seismic data classification (Vinther et al, 1995;Vinther, 1997;West et al, 2002;Gao, 2003). In this study, the attributes used are the inertia for several relative positions.…”

Section: Attributesmentioning

confidence: 99%

Coherent noise suppression by learning and analyzing the morphology of the data

2017

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We have developed a method for suppressing coherent noise from seismic data by using the morphological differences between the noise and the signal. This method consists of three steps: First, we applied a dictionary learning method on the data to extract a redundant dictionary in which the morphological diversity of the data is stored. Such a dictionary is a set of unit vectors called atoms that represent elementary patterns that are redundant in the data. Because the dictionary is learned on data contaminated by coherent noise, it is a mix of atoms representing signal patterns and atoms representing noise patterns. In the second step, we separate the noise atoms from the signal atoms using a statistical classification. Hence, the learned dictionary is divided into two subdictionaries: one describing the morphology of the noise and the other one describing the morphology of the signal. Finally, we separate the seismic signal and the coherent noise via morphological component analysis (MCA); it uses sparsity with respect to the two subdictionaries to identify the signal and the noise contributions in the mixture. Hence, the proposed method does not use prior information about the signal and the noise morphologies, but it entirely adapts to the signal and the noise of the data. It does not require a manual search for adequate transforms that may sparsify the signal and the noise, in contrast to existing MCA-based methods. We develop an application of the proposed method for removing the mechanical noise from a marine seismic data set. For mechanical noise that is coherent in space and time, the results show that our method provides better denoising in comparison with the standard FX-Decon, FX-Cadzow, and the curvelet-based denoising methods.

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