Abstract
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.
Highlights
On this basis, the time-space filtering method was developed and widely used. e typical method is spectral subtraction (SS), which is a transform domain filtering method. e SS has been widely used in enhancing speech signal due to its advantages of good denoising function and fast calculation speed [7, 8]
Because of the complexity of the noise, it is difficult to keep the stability in the time domain, which will lead to the error of noise spectrum estimation
Erefore, the TF spectral subtraction was optimized in the following: (1) according to the difference in trigger and duration between seismic data and voice data, the time window selection position of seismic data noise spectrum estimation is adjusted adaptively; (2) based on the dominate frequency of detection data, the filtering window function of seismic forward-prospecting is improved by adopting a weighting coefficient in the frequency domain; and (3) according to the empirical formula of optimal window function size of Gaussian filter, the optimal window length of time-frequency spectral subtraction (TF-SS) is selected
Summary
The time-space filtering method was developed and widely used. e typical method is spectral subtraction (SS), which is a transform domain filtering method. e SS has been widely used in enhancing speech signal due to its advantages of good denoising function and fast calculation speed [7, 8]. The time-space filtering method was developed and widely used. To solve the music noise caused by the location estimation error of effective data and noise distribution, Pascal and Filho [16] and Plapous et al [17] estimated the data position by prior estimation and voice detection and Zhang et al.[18], Cao [19], Ahmed [20], and Ye [21] used neural network and other methods to detect the effective signal boundary and improve data quality. E seismic data in TF involve more information than those in frequency domain. A time-frequency spectral subtraction (TF-SS) method for improving seismic data by removing random noise was studied. According to the characteristics of effective signal duration in seismic data and voice noise, the selection of TF noise spectrum and adaptive filtering coefficient is improved.
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