Curvelet-based seismic data processing: A multiscale and nonlinear approach

Herrmann, Felix J.; Wang, Deli; Hennenfent, Gilles; Moghaddam, Peyman P.

doi:10.1190/1.2799517

Cited by 168 publications

(57 citation statements)

References 26 publications

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“…However, complicated signals could not be well represented by the orthogonal basis used in the methods above. Multiscale geometric analysis methods (e.g., Rigielet, Contourlet and Curvelet [5,6]) have been favored in recent years. By neglecting the orthogonality and completeness, complicated signals could be well represented by inducing a lot of redundancy component.…”

Section: Introductionmentioning

confidence: 99%

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Tian

2017

Algorithms

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Abstract:We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster's sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

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Section: Introductionmentioning

confidence: 99%

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Tian

2017

Algorithms

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“…Wavefield decomposition can also be interpreted as an inverse problem. To preserve the full frequency content in an optimal sense and remove background noise, we solve this problem with a sparsity promoting inversion scheme in the curvelet domain, which has proven succesfull in a range of geophysical applications (Herrmann et al, 2008). The proposed methodology is demonstrated on synthetic data of a horizontal borehole placed in a heterogeneous medium.…”

Section: Introductionmentioning

confidence: 99%

Up/Down Wavefield Decomposition by Sparse Inversion

Neut¹,

Herrmann

2012

Proceedings

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SUMMARYExpressions have been derived for the decomposition of multi-component seismic recordings into up-and downgoing constituents. However, these expressions contain singularities at critical angles and can be sensitive for noise. By interpreting wavefield decomposition as an inverse problem and imposing constraints on the sparseness of the solution, we arrive at a robust formalism that can be applied to noisy data. The method is demonstrated on synthetic data with multi-component receivers in a horizontal borehole, but can also be applied for different configurations, including OBC and dual-sensor streamers.

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“…Herrmann et al, 2008;Kumar et al, 2011;Neelamani et al, 2008) already proved robustness of Discrete Curvelet Transform (DCT) (Candès et al, 2006) for attenuating random noise in seismic data. Here we demonstrate curvelet-based noise attenuation approach by applying 2D DCT to 3D post-stack seismic data acquired in the Flin Flon mining camp (White et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Application of Curvelet Denoising to 3D Post-stack Data Acquired in Hardrock Environment

Górszczyk¹,

Malinowski²

2014

Proceedings

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SUMMARYSeismic data acquired in hardrock environment are demanding for processing. Frequently occurring lack of clear coherent events hinders imaging and interpretation. Additional difficulty arises from the presence of significant amount of cultural noise (e.g. associated with exploration and processing of ore) which corrupt the data. For this purpose we demonstrate our noise attenuation approach based on 2D Discrete Curvelet Transform (DCT) by applying it to 3D post-stack seismic data from active mining camp. DCT introduce minimal overlapping between coefficients representing signal and noise in the curvelet domain, hence being well-suited for data denoising. Forward DCT is applied in sequences to inline, crossline and time slice sections. 3D DMO volume after curvelet denoising is much easier to interpret, e.g. it's easier to follow diffracted energy originating at ore lenses. We believe that the presented approach of running 2D DCT for 3D data might be also a sufficient substitution for a more expensive 3D DCT.

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Curvelet-based seismic data processing: A multiscale and nonlinear approach

Cited by 168 publications

References 26 publications

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Up/Down Wavefield Decomposition by Sparse Inversion

Application of Curvelet Denoising to 3D Post-stack Data Acquired in Hardrock Environment

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