Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation
Random noise attenuation in seismic data requires employing leading-edge methods to attain reliable denoised data. Efficient noise removal, effective signal preservation and recovery, reasonable processing time with a minimum signal distortion and seismic event deterioration are properties of a desi...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9743608/ |
| _version_ | 1818018221420183552 |
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| author | Javad Mafakheri Amin Roshandel Kahoo Rasoul Anvari Mokhtar Mohammadi Mohammad Radad Mehrdad Soleimani Monfared |
| author_facet | Javad Mafakheri Amin Roshandel Kahoo Rasoul Anvari Mokhtar Mohammadi Mohammad Radad Mehrdad Soleimani Monfared |
| author_sort | Javad Mafakheri |
| collection | DOAJ |
| description | Random noise attenuation in seismic data requires employing leading-edge methods to attain reliable denoised data. Efficient noise removal, effective signal preservation and recovery, reasonable processing time with a minimum signal distortion and seismic event deterioration are properties of a desired noise suppression algorithm. There are various noise attenuation methods available that more or less have these properties. We aim to obtain more effective denoised seismic data by assuming 3-D seismic data as a tensor in order three and increasing its dimension to 4-D seismic data by employing continuous wavelet transform (CWT). First, we map 3-D block seismic data to smaller blocks to estimate the low-rank component. The CWT of the tensor is calculated along the third dimension to extract the singular values and their related left/right vectors in the wavelet domain. Afterward, the effective low-rank component is extracted using optimized coefficients for each singular value. Thresholding is applied in the wavelet domain along the third dimension to calculate effective coefficients. Two synthetic and field data examples are considered for performance evaluation of the proposed method, and the results were compared with the competitive random noise suppression methods, such as the tensor optimum shrinkage singular value decomposition, the iterative block tensor singular value thresholding, and the block matching 4-D algorithms. Qualitative and quantitative comparison of the proposed method with other methods indicates that the proposed method efficiently eliminates random noise from seismic data. |
| first_indexed | 2024-04-14T07:37:11Z |
| format | Article |
| id | doaj.art-d0348a60cd1a464da9869f80f5605c26 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-14T07:37:11Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-d0348a60cd1a464da9869f80f5605c262022-12-22T02:05:40ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01152773278110.1109/JSTARS.2022.31627639743608Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank EstimationJavad Mafakheri0https://orcid.org/0000-0001-9098-6480Amin Roshandel Kahoo1https://orcid.org/0000-0002-2214-2558Rasoul Anvari2https://orcid.org/0000-0003-3093-0340Mokhtar Mohammadi3Mohammad Radad4https://orcid.org/0000-0002-3904-1999Mehrdad Soleimani Monfared5https://orcid.org/0000-0003-4755-4214School of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, IranSchool of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, IranSchool of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, IranDepartment of Information Technology, College of Enginering and Computer Science, Lebanese French University, Kurdistan Region, IraqSchool of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, IranSchool of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, IranRandom noise attenuation in seismic data requires employing leading-edge methods to attain reliable denoised data. Efficient noise removal, effective signal preservation and recovery, reasonable processing time with a minimum signal distortion and seismic event deterioration are properties of a desired noise suppression algorithm. There are various noise attenuation methods available that more or less have these properties. We aim to obtain more effective denoised seismic data by assuming 3-D seismic data as a tensor in order three and increasing its dimension to 4-D seismic data by employing continuous wavelet transform (CWT). First, we map 3-D block seismic data to smaller blocks to estimate the low-rank component. The CWT of the tensor is calculated along the third dimension to extract the singular values and their related left/right vectors in the wavelet domain. Afterward, the effective low-rank component is extracted using optimized coefficients for each singular value. Thresholding is applied in the wavelet domain along the third dimension to calculate effective coefficients. Two synthetic and field data examples are considered for performance evaluation of the proposed method, and the results were compared with the competitive random noise suppression methods, such as the tensor optimum shrinkage singular value decomposition, the iterative block tensor singular value thresholding, and the block matching 4-D algorithms. Qualitative and quantitative comparison of the proposed method with other methods indicates that the proposed method efficiently eliminates random noise from seismic data.https://ieeexplore.ieee.org/document/9743608/Continuous wavelet transform (CWT)low-rank matrixoptimal shrinkagesingular value decompositionseismic random noise |
| spellingShingle | Javad Mafakheri Amin Roshandel Kahoo Rasoul Anvari Mokhtar Mohammadi Mohammad Radad Mehrdad Soleimani Monfared Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Continuous wavelet transform (CWT) low-rank matrix optimal shrinkage singular value decomposition seismic random noise |
| title | Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation |
| title_full | Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation |
| title_fullStr | Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation |
| title_full_unstemmed | Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation |
| title_short | Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation |
| title_sort | expand dimensional of seismic data and random noise attenuation using low rank estimation |
| topic | Continuous wavelet transform (CWT) low-rank matrix optimal shrinkage singular value decomposition seismic random noise |
| url | https://ieeexplore.ieee.org/document/9743608/ |
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