矿业科学学报2025，Vol.10Issue(1)：105-115,11.DOI:10.19606/j.cnki.jmst.2024934
基于压缩奇异值分解的高效地震数据随机噪声压制
Accelerated random noise suppression of seismic data using compressed singular-value decomposition
摘要
Abstract
Random noise is one of the common background noises in seismic data,and its attenuation will directly affect the signal-to-noise ratio of seismic data,which is of great significance to improve the quality of seismic data.Low-rank approximation technique is a commonly used method to suppress ran-dom noise of seismic data.It converts frequency spatial domain data into the form of Hanke matrix,and uses singular value decomposition technique to reconstruct data by retaining large singular values,so as to achieve the purpose of rank reduction and suppress random noise.The method takes advantage of the low-rank nature of noiseless seismic data,which can be destroyed in the presence of random noise.However,traditional singular value decomposition technology has low computational efficiency,and seismic data generally consist of a large amount of datasets,so traditional singular value decomposition technology will inevitably lead to a large increase in time cost.In order to improve the efficiency of ran-dom noise suppression,a new singular value decomposition technique based on compressed sensing the-ory is proposed.The sparse representation of data is considered in the calculation of singular values,and the sparse representation of data is used to approximate the solution of high-dimensional singular vectors and singular values,so as to improve the accuracy and computational efficiency of singular val-ue decomposition.Compressed sensing theory makes full use of data sparsity,avoids direct processing of original high-dimensional data,and theoretically has high computational efficiency.Three-dimen-sional synthetic seismic records and field data examples are used to verify the validity and practicability of the proposed method,and comparisons with traditional and random singular value decomposition techniques are performed.The results show that the improved low-rank approximation technique can ef-fectively suppress random noise in seismic data,and the effective signal can be enhanced and highlight-ed.Compared with traditional and random singular value decomposition,the compressed singular value decomposition technique has higher computational efficiency and can greatly save time cost.Low-rank approximation technology based on compressed singular value decomposition has better performance than other methods in random noise suppression and can further improve the signal-to-noise ratio.关键词
低秩近似/奇异值分解/压缩感知/随机噪声Key words
low-rank approximation/singular-value decomposition/compressed sensing/random noise分类
矿业与冶金引用本文复制引用
孙超,林朋,刘育林,王秀东,徐东晶..基于压缩奇异值分解的高效地震数据随机噪声压制[J].矿业科学学报,2025,10(1):105-115,11.基金项目
国家重点研发计划专题(2022YFC2903705-03) （2022YFC2903705-03）
山东省煤田地质局科研专项(鲁煤地科字(2022)57号) （鲁煤地科字(2022）
国家自然科学基金(52394191,42104139) （52394191,42104139）
