Journal of Jilin University(Earth Science Edition) ›› 2021, Vol. 51 ›› Issue (4): 1231-1242.doi: 10.13278/j.cnki.jjuese.20200112

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Random Noise Suppression of Seismic Data with Scale-Oriented Adaptive Threshold Based on Shearlet Transform

Chen Yijun, Cheng Hao, Gong Enpu, Xue Lin   

  1. Key Laboratory of Safe Mining of Deep Metal Mines(Northeastern University), Ministry of Education, Shenyang 110819, China
  • Received:2020-05-04 Online:2021-07-26 Published:2021-08-02
  • Supported by:
    Supported by the National Natural Science Foundation of China (41804103) and the National Key R&D Program of China (2017YFC1503101)

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Abstract: The effective signal of seismic prospecting is often interfered by random noise, which is difficult to identify. It is necessary to separate the random noise from the effective signal. The traditional Shearlet global threshold does not change with the direction and scale, which leads to the loss of many effective signals during denoising. As a new multi-scale and multi-directional time-frequency analysis method, Shearlet transform has the best sparse representation ability, local feature, and direction sensitivity. In this paper, the noisy seismic signal is decomposed by Shearlet to calculate the L2 norm of Shearlet domain coefficients in various scales and directions. After rearranging the data, it is found that the L2 norm decreases as the direction changes. Based on the L2 norm, a calculation method of adaptive threshold value of the scale direction of the number is proposed. Compared with wavelet transform, curvelet transform, and Shearlet global threshold denoising methods, the denoising results of simulated data and actual seismic records show that the method in this paper can remove the random noise and can also recover the weak deep signals well, and the signal noise ratio of the data is improved from 0.929 9 dB to 11.565 1 dB.

Key words: Shearle, seismic data, threshold improvement, signal noise ratio

CLC Number: 

  • P631.4
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