Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain
Random noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much lower, which brings great difficulty to seism...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8939375/ |
| _version_ | 1828977946346389504 |
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| author | Yu Sang Jinguang Sun Xiangfu Meng Haibo Jin Yanfei Peng Xinjun Zhang |
| author_facet | Yu Sang Jinguang Sun Xiangfu Meng Haibo Jin Yanfei Peng Xinjun Zhang |
| author_sort | Yu Sang |
| collection | DOAJ |
| description | Random noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much lower, which brings great difficulty to seismic data processing and interpretation. To this end, we propose an unconventional and effective seismic random noise attenuation method based on proximal classifier with consistency (PCC) in transform domain. Firstly, we analyze various transforms for seismic data from traditional wavelet transform and curvelet transform to emerging non-subsampled shearlet transform (NSST) and non-subsampled contourlet transform (NSCT). And, we select the excellent NSST to decompose the noisy seismic data into different sub-bands of frequency and orientation responses. Secondly, unlike traditional sparse transform based seismic denoising methods that often directly use a thresholding operator and corresponding inverse transform to denoise seismic data, our proposed method employs a superior performance PCC to classify the NSST coefficients of seismic data before thresholding operator. The added step can effectively divide the NSST coefficients into reflected useful signal coefficients and noise-related coefficients, which can preserve the edge of reflected signals and keep the information of events intact as much as possible. In addition, we also introduce an adaptive threshold computing method and a soft-thresholding method to achieve seismic data denoising better. Finally, the experimental results on the typical synthetic example and real seismic data show the superior performance of the proposed method. |
| first_indexed | 2024-12-14T15:08:01Z |
| format | Article |
| id | doaj.art-8e8501b6408b4d7cb95ec04e259d16e1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:08:01Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8e8501b6408b4d7cb95ec04e259d16e12022-12-21T22:56:39ZengIEEEIEEE Access2169-35362020-01-018303683037710.1109/ACCESS.2019.29590248939375Seismic Random Noise Attenuation Based on PCC Classification in Transform DomainYu Sang0https://orcid.org/0000-0002-4596-911XJinguang Sun1https://orcid.org/0000-0001-5936-6697Xiangfu Meng2https://orcid.org/0000-0001-5260-1105Haibo Jin3Yanfei Peng4https://orcid.org/0000-0001-7322-6623Xinjun Zhang5https://orcid.org/0000-0002-8818-1808School of Electronic and Information Engineering, Liaoning Technical University, Huludao, ChinaSchool of Electronic and Information Engineering, Liaoning Technical University, Huludao, ChinaSchool of Electronic and Information Engineering, Liaoning Technical University, Huludao, ChinaSchool of Software, Liaoning Technical University, Huludao, ChinaSchool of Electronic and Information Engineering, Liaoning Technical University, Huludao, ChinaSchool of Electronic and Information Engineering, Liaoning Technical University, Huludao, ChinaRandom noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much lower, which brings great difficulty to seismic data processing and interpretation. To this end, we propose an unconventional and effective seismic random noise attenuation method based on proximal classifier with consistency (PCC) in transform domain. Firstly, we analyze various transforms for seismic data from traditional wavelet transform and curvelet transform to emerging non-subsampled shearlet transform (NSST) and non-subsampled contourlet transform (NSCT). And, we select the excellent NSST to decompose the noisy seismic data into different sub-bands of frequency and orientation responses. Secondly, unlike traditional sparse transform based seismic denoising methods that often directly use a thresholding operator and corresponding inverse transform to denoise seismic data, our proposed method employs a superior performance PCC to classify the NSST coefficients of seismic data before thresholding operator. The added step can effectively divide the NSST coefficients into reflected useful signal coefficients and noise-related coefficients, which can preserve the edge of reflected signals and keep the information of events intact as much as possible. In addition, we also introduce an adaptive threshold computing method and a soft-thresholding method to achieve seismic data denoising better. Finally, the experimental results on the typical synthetic example and real seismic data show the superior performance of the proposed method.https://ieeexplore.ieee.org/document/8939375/Seismic datarandom noiseattenuationproximal classifier with consistency (PCC)non-subsampled shearlet transform (NSST) |
| spellingShingle | Yu Sang Jinguang Sun Xiangfu Meng Haibo Jin Yanfei Peng Xinjun Zhang Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain IEEE Access Seismic data random noise attenuation proximal classifier with consistency (PCC) non-subsampled shearlet transform (NSST) |
| title | Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain |
| title_full | Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain |
| title_fullStr | Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain |
| title_full_unstemmed | Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain |
| title_short | Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain |
| title_sort | seismic random noise attenuation based on pcc classification in transform domain |
| topic | Seismic data random noise attenuation proximal classifier with consistency (PCC) non-subsampled shearlet transform (NSST) |
| url | https://ieeexplore.ieee.org/document/8939375/ |
| work_keys_str_mv | AT yusang seismicrandomnoiseattenuationbasedonpccclassificationintransformdomain AT jinguangsun seismicrandomnoiseattenuationbasedonpccclassificationintransformdomain AT xiangfumeng seismicrandomnoiseattenuationbasedonpccclassificationintransformdomain AT haibojin seismicrandomnoiseattenuationbasedonpccclassificationintransformdomain AT yanfeipeng seismicrandomnoiseattenuationbasedonpccclassificationintransformdomain AT xinjunzhang seismicrandomnoiseattenuationbasedonpccclassificationintransformdomain |