A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain

The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order sy...
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Gespeichert in:

Autor*in:	Pingping Bing [verfasserIn] Wei Liu [verfasserIn] Zhihua Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	High-order synchrosqueezing transform robust principal component analysis low rank matrix noise suppression

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 183546-183556
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:183546-183556

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3028145

Katalog-ID:	DOAJ006471900

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520			\|a The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation.
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allfields	10.1109/ACCESS.2020.3028145 doi (DE-627)DOAJ006471900 (DE-599)DOAJffefff49e17c4020885d612d15b1f487 DE-627 ger DE-627 rakwb eng TK1-9971 Pingping Bing verfasserin aut A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation. High-order synchrosqueezing transform robust principal component analysis low rank matrix noise suppression Electrical engineering. Electronics. Nuclear engineering Wei Liu verfasserin aut Zhihua Zhang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 183546-183556 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:183546-183556 https://doi.org/10.1109/ACCESS.2020.3028145 kostenfrei https://doaj.org/article/ffefff49e17c4020885d612d15b1f487 kostenfrei https://ieeexplore.ieee.org/document/9210485/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 183546-183556
spelling	10.1109/ACCESS.2020.3028145 doi (DE-627)DOAJ006471900 (DE-599)DOAJffefff49e17c4020885d612d15b1f487 DE-627 ger DE-627 rakwb eng TK1-9971 Pingping Bing verfasserin aut A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation. High-order synchrosqueezing transform robust principal component analysis low rank matrix noise suppression Electrical engineering. Electronics. Nuclear engineering Wei Liu verfasserin aut Zhihua Zhang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 183546-183556 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:183546-183556 https://doi.org/10.1109/ACCESS.2020.3028145 kostenfrei https://doaj.org/article/ffefff49e17c4020885d612d15b1f487 kostenfrei https://ieeexplore.ieee.org/document/9210485/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 183546-183556
allfields_unstemmed	10.1109/ACCESS.2020.3028145 doi (DE-627)DOAJ006471900 (DE-599)DOAJffefff49e17c4020885d612d15b1f487 DE-627 ger DE-627 rakwb eng TK1-9971 Pingping Bing verfasserin aut A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation. High-order synchrosqueezing transform robust principal component analysis low rank matrix noise suppression Electrical engineering. Electronics. Nuclear engineering Wei Liu verfasserin aut Zhihua Zhang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 183546-183556 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:183546-183556 https://doi.org/10.1109/ACCESS.2020.3028145 kostenfrei https://doaj.org/article/ffefff49e17c4020885d612d15b1f487 kostenfrei https://ieeexplore.ieee.org/document/9210485/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 183546-183556
allfieldsGer	10.1109/ACCESS.2020.3028145 doi (DE-627)DOAJ006471900 (DE-599)DOAJffefff49e17c4020885d612d15b1f487 DE-627 ger DE-627 rakwb eng TK1-9971 Pingping Bing verfasserin aut A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation. High-order synchrosqueezing transform robust principal component analysis low rank matrix noise suppression Electrical engineering. Electronics. Nuclear engineering Wei Liu verfasserin aut Zhihua Zhang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 183546-183556 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:183546-183556 https://doi.org/10.1109/ACCESS.2020.3028145 kostenfrei https://doaj.org/article/ffefff49e17c4020885d612d15b1f487 kostenfrei https://ieeexplore.ieee.org/document/9210485/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 183546-183556
allfieldsSound	10.1109/ACCESS.2020.3028145 doi (DE-627)DOAJ006471900 (DE-599)DOAJffefff49e17c4020885d612d15b1f487 DE-627 ger DE-627 rakwb eng TK1-9971 Pingping Bing verfasserin aut A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation. High-order synchrosqueezing transform robust principal component analysis low rank matrix noise suppression Electrical engineering. Electronics. Nuclear engineering Wei Liu verfasserin aut Zhihua Zhang verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 183546-183556 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:183546-183556 https://doi.org/10.1109/ACCESS.2020.3028145 kostenfrei https://doaj.org/article/ffefff49e17c4020885d612d15b1f487 kostenfrei https://ieeexplore.ieee.org/document/9210485/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 183546-183556
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callnumber-first	T - Technology
author	Pingping Bing
spellingShingle	Pingping Bing misc TK1-9971 misc High-order synchrosqueezing transform misc robust principal component analysis misc low rank matrix misc noise suppression misc Electrical engineering. Electronics. Nuclear engineering A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain
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topic_title	TK1-9971 A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain High-order synchrosqueezing transform robust principal component analysis low rank matrix noise suppression
topic	misc TK1-9971 misc High-order synchrosqueezing transform misc robust principal component analysis misc low rank matrix misc noise suppression misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc High-order synchrosqueezing transform misc robust principal component analysis misc low rank matrix misc noise suppression misc Electrical engineering. Electronics. Nuclear engineering
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title	A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain
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title_full	A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain
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title_sort	robust random noise suppression method for seismic data using sparse low-rank estimation in the time-frequency domain
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title_auth	A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain
abstract	The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation.
abstractGer	The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation.
abstract_unstemmed	The noise separation from seismic data is of significant importance in geophysics. In most cases, the random noise always overlaps the seismic reflections over time, which makes it challenging to suppress. To enhance seismic signal, we propose a robust noise suppression method based on high-order synchrosqueezing transform (FSSTH) and robust principal component analysis (RPCA). Firstly, the noisy seismic data is transformed into a sparse time-frequency matrix (TFM) using the FSSTH. Then, the RPCA algorithm is employed to decompose the sparse TFM into a low-rank matrix and a sparse matrix that can be used to depict the useful signal and noise, respectively. Finally, the denoised signal can be obtained by back-transforming the low-rank matrix to the time domain via the inverse FSSTH. We utilize a synthetic data and two field datasets to demonstrate the robustness and superiority of our method, and compare with the conventional denoising algorithms such as f-x denconvolution and f-x$ singular spectrum analysis (SSA). The obtained results indicate that the proposed method is capable of achieving an excellent tadeoff between random noise suppression and seismic signal preservation.
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title_short	A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain
url	https://doi.org/10.1109/ACCESS.2020.3028145 https://doaj.org/article/ffefff49e17c4020885d612d15b1f487 https://ieeexplore.ieee.org/document/9210485/ https://doaj.org/toc/2169-3536
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up_date	2024-07-03T21:07:50.011Z
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A Robust Random Noise Suppression Method for Seismic Data Using Sparse Low-Rank Estimation in the Time-Frequency Domain

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