A random noise suppression with 2D non-uniform curvelet transform

被引：0
|
作者：
Zhang H. [1 ]
Diao S. [1 ]
Wen J. [2 ]
Huang G. [1 ]
Zhu W. [1 ]
Bai M. [3 ]
机构：
[1] Fundamental Science on Radioactive Geology and Exploration Technology Laboratory, East China University of Technology, Nanchang, 330013, Jiangxi
[2] Shanxi Provincial Coal Geological Exploration, Geophysical Prospecting, Surveying and Mapping Institute, Jinzhong, 030600, Shanxi
[3] School of Resources and Environment, North China University of Water Resources and Electric Power, Zhengzhou, 450046, Henan
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2019年 / 54卷 / 01期
关键词：
Multi-scale; Noise suppression; Non-uniform curvelet transform; Non-uniform fast Fourier transform; Threshold;
D O I：
10.13810/j.cnki.issn.1000-7210.2019.01.003
中图分类号：
学科分类号：
摘要：
Real seismic data are often non-uniformly sampled due to limits of field acquisition environment and terrain conditions. But conventional curvelet-based denoising cannot process non-uniform sampled and noisy data. We first introduce the non-uniform fast Fourier transform (NFFT) in the multi-scale and multi-directional curvelet transform, and construct the regularized inversion of operator that takes the uniformly sampled curvelet coefficients to non-uniformly data.Then we use linearized Bregman method for the inversion calculation, and adopt soft thresholds to remove noise of the curvelet coefficients in each iteration, and get the noise free uniform curvelet coefficients. Finally we perform the conventional inverse fast discrete curvelet transform (FDCT) and get denoised seismic data. Tests on synthetic and real data reveal that the proposed method can better suppress random noise when it interpolates non-uniformly sampled data to uniformly sampled data. © 2019, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.
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页码：16 / 23
页数：7
相关论文
共 29 条
  • [1] Tian Y., Wang X., Peng G., Et al., Noise attenuation technology on wide azimuth seismic data, Oil Geophysical Prospecting, 48, 2, pp. 187-191, (2013)
  • [2] Liu Z., Xie Y., Chen F., Discussion on noise attenuation methods in seismic data pro-cessing, Oil Geophysical Prospecting, 44, pp. 67-71, (2009)
  • [3] Qiu S., Liu X., Liu S., An image denoising method based on PDE and structure-texture, Journal of East China Institute of Technology, 36, 1, pp. 90-94, (2013)
  • [4] Liu Y., Fomel S., Liu C., Signal and noise separation in prestack seismic data using velocity-dependent seislet transform, Geophysics, 80, 6, pp. WD117-WD128, (2015)
  • [5] Zhang Y., Zhang H., Liu S., A study on 2D wavelet transform and median filtering jointed denoising method, Coal Geology of China, 23, 5, pp. 38-42, (2011)
  • [6] Chen Y., Ma J., Fomel S., Double-sparsity dictionary for seismic noise attenuation, Geophysics, 81, 2, pp. V17-V30, (2016)
  • [7] He X., Liu S., Tong Z., 3-D denoising method through visual speed in frequency-wavenumber domain, Journal of China University of Petroleum, 34, 4, pp. 62-66, (2010)
  • [8] Liu Y., Liu N., Liu C., Adaptive prediction filtering in t-x-y domain for random noise attenuation using regularized nonstationary autoregression, Geophysics, 80, 1, pp. V13-V21, (2015)
  • [9] Zhang H., Chen X., Yang H., Optimistic wavelet basis selection in seismic signal noise elimination, Oil Geophysical Prospecting, 46, 1, pp. 70-75, (2011)
  • [10] Wang Q., Zhang J., Jiang X., Et al., A robust denoise edge detection method based on high-dimensional wavelet transform, Oil Geophysical Prospecting, 51, 5, pp. 889-893, (2016)
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