Study on the Velocity Analysis Method of Low SNR Seismic Data


  •  Jianbo He    
  •  Zhenyu Wang    
  •  Mingdong Zhang    

Abstract

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-0569
  • ISSN(Online): 1927-0577
  • Started: 2011
  • Frequency: semiannual

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Google-based Impact Factor (2018): 13.22

h-index (January 2018): 12

i10-index (January 2018): 18

h5-index (January 2018): 9

h5-median(January 2018):14

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