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Denoising Method Based on Spectral Subtraction in Time-Frequency Domain
Author(s) -
Lei Hao,
Shuai Cao,
Pengfei Zhou,
Lei Chen,
Yi Zhang,
Kai Li,
Dongdong Xie,
Yijun Geng
Publication year - 2021
Publication title -
advances in civil engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 25
eISSN - 1687-8094
pISSN - 1687-8086
DOI - 10.1155/2021/6621596
Subject(s) - subtraction , noise (video) , computer science , frequency domain , random noise , algorithm , time domain , signal (programming language) , basis (linear algebra) , noise reduction , key (lock) , time–frequency analysis , data processing , speech recognition , mathematics , artificial intelligence , filter (signal processing) , computer vision , arithmetic , geometry , image (mathematics) , programming language , operating system , computer security
In view of the key problem that a large amount of noise in seismic data can easily induce false anomalies and interpretation errors in seismic exploration, the time-frequency spectrum subtraction (TF-SS) method is adopted into data processing to reduce random noise in seismic data. On this basis, the main frequency information of seismic data is calculated and used to optimize the filtering coefficients. According to the characteristics of effective signal duration between seismic data and voice data, the time-frequency spectrum selection method and filtering coefficient are modified. In addition, simulation tests were conducted by using different S/R, which indicates the effectiveness of the TF-SS in removing the random noise.

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