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FrequencySpace-Dependent Smoothing Regularized Nonstationary Predictive Filtering

文献类型：期刊论文

作者Huang, Guangtan1; Bai, Min2; Wang, Hang3; Liu, Xingye4; Chen, Yangkang3
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2022
卷号60页码:9
关键词Smoothing methods Transforms Noise reduction Mathematical model Frequency-domain analysis Predictive models Inverse problems Predictive filtering seismic data signa processing
ISSN号0196-2892
DOI10.1109/TGRS.2021.3064945
英文摘要Predictive filtering is one of the most widely used denoising algorithms in the seismic data processing community because of its high efficiency and stability in different situations. The traditional predictive filtering, however, is not able to deal with structurally complex data set unless applied in local windows. We develop a novel noncausal predictive filtering method that is free of the windowing step but is able to denoise complicated data set. We extend the stationary predictive filtering method to its nonstationary version, where the predictive filter coefficients vary across the frequency-space domain. The nonstationary predictive filtering (NPF) model requires solving a highly underdetermined inverse problem using an iterative shaping regularization method. The traditional shaping regularization method solves an inverse problem by applying a constant smoothing operator and thus does not consider the heterogeneity of the filter coefficients in the frequencyx2013;space domain. We propose to apply a nonstationary smoothing operator to constrain the model in the shaping regularization framework. The smoothing radius in the nonstationary smoothing operator is chosen based on a priori information of the model, e.g., the nonstationarity of the data in the frequencyx2013;space domain. The proposed NPF method offers the flexibility in controlling the smoothness and sharpness of the calculated filter coefficients in both frequency and space dimensions. Several synthetic data sets and complicated real data examples are used to demonstrate the advantages of the new method.
资助项目Starting Fund of Zhejiang University ; National Natural Science Foundation of China[41704121]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000730619400049
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.198/handle/2S6PX9GI/30830]  
专题中科院武汉岩土力学所
通讯作者Bai, Min
作者单位1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
2.Yangtze Univ, Key Lab Explorat Technol Oil & Gas Resources, Minist Educ, Wuhan 430100, Peoples R China
3.Zhejiang Univ, Sch Earth Sci, Key Lab Geosci Big Data & Deep Resource Zhejiang, Hangzhou 310027, Peoples R China
4.Xian Univ Sci & Technol, Coll Geol & Environm, Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Guangtan,Bai, Min,Wang, Hang,et al. FrequencySpace-Dependent Smoothing Regularized Nonstationary Predictive Filtering[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:9.
APA Huang, Guangtan,Bai, Min,Wang, Hang,Liu, Xingye,&Chen, Yangkang.(2022).FrequencySpace-Dependent Smoothing Regularized Nonstationary Predictive Filtering.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,9.
MLA Huang, Guangtan,et al."FrequencySpace-Dependent Smoothing Regularized Nonstationary Predictive Filtering".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):9.

入库方式： OAI收割

来源：武汉岩土力学研究所

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