Shuaishuai Li, Xiaotong Zhang, Jiangjie Zhang, Linong Liu
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In the denoising process of the ADDF, seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in both synthetic examples and field data when compared to two typical denoising methods: f-x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, the ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"31 3","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive dual-domain filtering for random seismic noise removal\",\"authors\":\"Shuaishuai Li, Xiaotong Zhang, Jiangjie Zhang, Linong Liu\",\"doi\":\"10.1190/geo2023-0532.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Random noise significantly reduces the signal-to-noise ratio (S/N) of seismic data and affects the accuracy of seismic interpretation. Traditional denoising methods typically require manual parameter tuning to increase the robustness and accuracy across various random noise levels. In this study, based on the statistical definition of random noise, we used the variance of random noise as the level of random noise and proposed an adaptive dual-domain filter (ADDF). The ADDF method estimates the random noise variance in the seismic data and uses this estimation to effectively denoise the seismic data. First, we employ a difference operator in two directions to remove useful structures from the seismic data. The processed data are then used to estimate the global random noise variance through iterative statistical processing. In the denoising process of the ADDF, seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in both synthetic examples and field data when compared to two typical denoising methods: f-x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, the ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":\"31 3\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1190/geo2023-0532.1\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1190/geo2023-0532.1","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Adaptive dual-domain filtering for random seismic noise removal
Random noise significantly reduces the signal-to-noise ratio (S/N) of seismic data and affects the accuracy of seismic interpretation. Traditional denoising methods typically require manual parameter tuning to increase the robustness and accuracy across various random noise levels. In this study, based on the statistical definition of random noise, we used the variance of random noise as the level of random noise and proposed an adaptive dual-domain filter (ADDF). The ADDF method estimates the random noise variance in the seismic data and uses this estimation to effectively denoise the seismic data. First, we employ a difference operator in two directions to remove useful structures from the seismic data. The processed data are then used to estimate the global random noise variance through iterative statistical processing. In the denoising process of the ADDF, seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in both synthetic examples and field data when compared to two typical denoising methods: f-x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, the ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
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