Seismic data de-noising method based on VMD in time-frequency domain

被引：0
|
作者：
Hu R. [1 ]
He J. [1 ,2 ]
Li H. [1 ]
Zhang X. [1 ]
Pei J. [1 ]
Liu Y. [1 ]
机构：
[1] Korla Branch, Geophysical Research Institute, BGP Inc., CNPC, Korla
[2] College of Earth Science and Engineering, Xi'an Shiyou University, Xi'an
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2021年 / 56卷 / 02期
关键词：
Noise attenuation; Time-frequency analysis; Variable mode decomposition;
D O I：
10.13810/j.cnki.issn.1000-7210.2021.02.006
中图分类号：
学科分类号：
摘要：
Strong noise interference is the primary factor that causes poor imaging of deep seismic data. A new idea applies a variable mode decomposition algorithm to noise suppression. Firstly, the analytical signals of seismic data are constructed by Hilbert-Huang transform (HHT), then the seismic data are converted into time-frequency domain where time-frequency slices are decomposed as instrinsic mode functions (IMFs) by the variable mode decomposition algorithm; then the energy distribution of effective signals and noises on the time-frequency slices is analyzed, and the time-frequency slices are reconstructed by the effective IMFs; and finally the slices are transformed back to the space-time domain to achieve the goal of noise suppression. The control of key parameters on the denoising effect of the algorithm has been analyzed on model data. The results of actual data have verified that the algorithm can effectively suppress strong random noises, and it is also effective for suppressing linear noises. © 2021, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.
引用
收藏
页码：257 / 264
页数：7
相关论文
共 27 条
  • [1] Han J, van der Baan M., Empirical mode decomposition for seismic time-frequency analysis, Geophysics, 78, 2, pp. O9-O19, (2013)
  • [2] Liu J, Marfurt K J., Instantaneous spectral attributes to detect channels, Geophysics, 72, 2, pp. P23-P31, (2007)
  • [3] Liu Y, Fomel S., Seismic data analysis using local time-frequency decomposition, Geophysical Prospecting, 61, 3, pp. 516-525, (2013)
  • [4] Tary J B, Herrera R H, Han J, Et al., Spectral estimation: What is new? What is next?, Reviews of Geo-physics, 52, 4, pp. 723-749, (2014)
  • [5] Morlet J., Wave propagation and sampling theory, Geophysics, 47, 2, pp. 222-236, (1982)
  • [6] Candes E J, Donoho D L., Ridgelets: A key to higher-dimensional intermittency, Philosophical Transactions of the Royal Society A, 357, 1760, pp. 2495-2509, (1999)
  • [7] BAO Qianzong, GAO Jinghuai, CHEN Wenchao, Wavefield separation in vertical seismic profiles in Curvelet domain, Journal of Xi'an Jiaotong University, 41, 6, pp. 650-654, (2007)
  • [8] Neelamani R, Baumstein A I, Gillard D G, Et al., Coherent and random noise attenuation using the curvelet transform, The Leading Edge, 27, 2, pp. 240-248, (2008)
  • [9] MENG Gege, WANG Deli, CHEN Xin, Research on the denoising method of seismic data based on 3D curvielet transform, Geophysical Prospecting for Petroleum, 53, 3, pp. 313-323, (2014)
  • [10] ZHANG Henglei, ZHANG Yuncui, SONG Shuang, Et al., Denoising method for pre-stack seismic data based on Curvelet domain, Oil Geophysical Prospecting, 43, 5, pp. 508-513, (2008)
123