Seismic Multichannel Deconvolution via 2-D K-SVD and MSD-oCSC

The deconvolution method is crucial for enhancing seismic resolution. Traditional multichannel schemes incorporate lateral constraints to enhance data continuity and integrity. However, a predominant challenge is that many existing methods assume that the estimated model parameters have certain feat...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 62; S. 1 - 13
Hauptverfasser:	Wang, Yaojun, Gao, Xiayu, Zhang, Guiqian, Zou, Bangli, Hu, Guangmin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	2-D K-means singular value decomposition (K-SVD) Algorithms Coding Components Convolution Convolutional codes convolutional sparse coding (CSC) Decomposition Deconvolution Dictionaries Encoding Feature maps Frequencies Glossaries Image coding Iterative methods Linear programming multichannel Objective function Reflectivity Regularization Segments Seismic analysis Seismic data seismic deconvolution Seismic profiles Seismology Singular value decomposition
ISSN:	0196-2892, 1558-0644
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Abstract	The deconvolution method is crucial for enhancing seismic resolution. Traditional multichannel schemes incorporate lateral constraints to enhance data continuity and integrity. However, a predominant challenge is that many existing methods assume that the estimated model parameters have certain features, which are always inconsistent with the true situation. In this article, we present a novel data-driven multichannel seismic deconvolution method based on convolutional sparse coding (CSC) and 2-D <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means singular value decomposition (K-SVD). We partition the seismic profile into low- and high-frequency segments, addressing CSC's limitation in representing low-frequency components effectively. We leverage 2-D K-SVD for extracting lateral features in low-frequency components and apply CSC to the high-frequency segments. Distinctively, our method accounts for the lateral characteristics within both frequency bands during decomposition, a notable advancement over conventional filtering-based techniques. It ensures the preserved convolutional relationship in frequency separation. We enhance the conventional 1-D K-SVD with 2-D sample patches, evolving it into a more adept 2-D K-SVD dictionary for low-frequency components. We apply this to multichannel deconvolution regularization, enabling low-frequency seismic data deconvolution. To address the challenge of information fragmentation in high-frequency components caused by block-based sparse coding, the orthogonal constrains are added into the feature map instead of the image, making the convolutional dictionary learning more precise than the conventional CSC algorithm. This refinement is integral to optimizing the high-frequency deconvolution objective function. In the final step, iterative processing of both frequency components yields the refined multichannel seismic deconvolution via orthogonal convolution sparse coding (MSD-oCSC). Our method's efficacy is corroborated through rigorous model tests and real data applications.
AbstractList	The deconvolution method is crucial for enhancing seismic resolution. Traditional multichannel schemes incorporate lateral constraints to enhance data continuity and integrity. However, a predominant challenge is that many existing methods assume that the estimated model parameters have certain features, which are always inconsistent with the true situation. In this article, we present a novel data-driven multichannel seismic deconvolution method based on convolutional sparse coding (CSC) and 2-D <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means singular value decomposition (K-SVD). We partition the seismic profile into low- and high-frequency segments, addressing CSC's limitation in representing low-frequency components effectively. We leverage 2-D K-SVD for extracting lateral features in low-frequency components and apply CSC to the high-frequency segments. Distinctively, our method accounts for the lateral characteristics within both frequency bands during decomposition, a notable advancement over conventional filtering-based techniques. It ensures the preserved convolutional relationship in frequency separation. We enhance the conventional 1-D K-SVD with 2-D sample patches, evolving it into a more adept 2-D K-SVD dictionary for low-frequency components. We apply this to multichannel deconvolution regularization, enabling low-frequency seismic data deconvolution. To address the challenge of information fragmentation in high-frequency components caused by block-based sparse coding, the orthogonal constrains are added into the feature map instead of the image, making the convolutional dictionary learning more precise than the conventional CSC algorithm. This refinement is integral to optimizing the high-frequency deconvolution objective function. In the final step, iterative processing of both frequency components yields the refined multichannel seismic deconvolution via orthogonal convolution sparse coding (MSD-oCSC). Our method's efficacy is corroborated through rigorous model tests and real data applications. The deconvolution method is crucial for enhancing seismic resolution. Traditional multichannel schemes incorporate lateral constraints to enhance data continuity and integrity. However, a predominant challenge is that many existing methods assume that the estimated model parameters have certain features, which are always inconsistent with the true situation. In this article, we present a novel data-driven multichannel seismic deconvolution method based on convolutional sparse coding (CSC) and 2-D [Formula Omitted]-means singular value decomposition (K-SVD). We partition the seismic profile into low- and high-frequency segments, addressing CSC’s limitation in representing low-frequency components effectively. We leverage 2-D K-SVD for extracting lateral features in low-frequency components and apply CSC to the high-frequency segments. Distinctively, our method accounts for the lateral characteristics within both frequency bands during decomposition, a notable advancement over conventional filtering-based techniques. It ensures the preserved convolutional relationship in frequency separation. We enhance the conventional 1-D K-SVD with 2-D sample patches, evolving it into a more adept 2-D K-SVD dictionary for low-frequency components. We apply this to multichannel deconvolution regularization, enabling low-frequency seismic data deconvolution. To address the challenge of information fragmentation in high-frequency components caused by block-based sparse coding, the orthogonal constrains are added into the feature map instead of the image, making the convolutional dictionary learning more precise than the conventional CSC algorithm. This refinement is integral to optimizing the high-frequency deconvolution objective function. In the final step, iterative processing of both frequency components yields the refined multichannel seismic deconvolution via orthogonal convolution sparse coding (MSD-oCSC). Our method’s efficacy is corroborated through rigorous model tests and real data applications.
Author	Wang, Yaojun Zou, Bangli Zhang, Guiqian Hu, Guangmin Gao, Xiayu
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SubjectTerms	2-D K-means singular value decomposition (K-SVD) Algorithms Coding Components Convolution Convolutional codes convolutional sparse coding (CSC) Decomposition Deconvolution Dictionaries Encoding Feature maps Frequencies Glossaries Image coding Iterative methods Linear programming multichannel Objective function Reflectivity Regularization Segments Seismic analysis Seismic data seismic deconvolution Seismic profiles Seismology Singular value decomposition
Title	Seismic Multichannel Deconvolution via 2-D K-SVD and MSD-oCSC
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