Cluster Computing

, Volume 22, Supplement 3, pp 6609–6620 | Cite as

Seismic data processing method based on wavelet transform for de-noising

  • Yong Lu
  • Yongming HuangEmail author
  • Wei Xue
  • Guobao Zhang


The earthquakes happened around the world have brought irreparable damage to humans. In order to reduce losses, scholars from all over the world are committed to studying the information contained in the magnetotelluric signals. The detecting instrument arranged in the field or in the Seismological Bureau station are usually interfered by noises, so the problem of how to remove these noise is the research content of this paper. This paper is based on the wavelet transform and fast Fourier transform, and it first uses the appropriate wavelet base to decompose the signal in multi-level, and then uses the fast Fourier transform to obtain the spectrum of signal in each layer, then uses the spectrum to select the appropriate decomposition order, and carries out threshold de-noising on signal, finally reconstructs and calculates the signal after noise reduction. In this paper, the principle and steps of dealing with the signal are given out, and the analog signal and measured signal are simulated through MATLAB, so as to verify the effectiveness. The results show that, the wavelet transform has good effect in dealing with the magnetotelluric signal, which can remove the noise signal well and keep the energy characteristic of the original signal, and is convenient for follow-up analysis.


Wavelet transform Seismic data processing Denoise 



This work was supported by Jiangsu Province Natural Science Foundation (No. BE2016805), The Spark Program of Earthquake Technology of CEA (No. XH17013) and National Natural Science Foundation (Nos. 61503081, 61473079).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yong Lu
    • 1
  • Yongming Huang
    • 2
    • 3
    Email author
  • Wei Xue
    • 2
    • 3
  • Guobao Zhang
    • 2
    • 3
  1. 1.Earthquake Administration of Jiangsu ProvinceNanjingChina
  2. 2.School of AutomationSoutheast UniversityNanjingChina
  3. 3.Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of EducationNanjingChina

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