Generalized stable inverse Q filtering

2019 ◽  
Vol 169 ◽  
pp. 214-225
Author(s):  
Yan Zhao ◽  
Ningbo Mao ◽  
Jing Xu
Keyword(s):  
2020 ◽  
Vol 223 (1) ◽  
pp. 488-501
Author(s):  
Guochang Liu ◽  
Chao Li ◽  
Ying Rao ◽  
Xiaohong Chen

SUMMARY Seismic attenuation is one of the main factors responsible for degradation of the resolution of seismic data. During seismic wave propagation in attenuation medium, the energy of signal components seriously decreases, especially those with higher frequencies. The seismic attenuation and resolution reduction are generally compensated for with inverse Q filtering in the frequency or time domain. However, the implementation of pre-stack inverse Q filtering is challenging because the traveltime in each layer is not easy to obtain for the pre-stack seismic gather, unless the accurate velocity model is known. In this study, we propose an inverse Q filtering method for the pre-stack seismic gather that uses the local slope and warped mapping to determine the propagation path, and Taylor-expansion-based division is used to stabilize the inversion. The local slope can determine the reflection events with the same ray path, and the inverse warped mapping can transform the attenuation factor from the ${t_0} - p$ (zero-offset traveltime to ray parameter) domain to the $t - x$ (traveltime and offset) domain. The attenuation factor in the ${t_0} - p$ domain is easy to calculate because the traveltimes and Q values in each layer are known. The proposed oriented pre-stack inverse Q filtering method is velocity-independent and suitable for a depth varying Q model. The synthetic and real data examples demonstrated that the method can effectively correct the attenuation and dispersion of seismic waves, and can obtain pre-stack seismic gathers with high resolution.


2015 ◽  
Vol 733 ◽  
pp. 152-155
Author(s):  
Shao Zhong Liu ◽  
Yu Wei Guo

Through the use of conventional inverse Q filtering algorithm can be applied to derive a stable and efficient inverse Q filtering algorithm continuous medium, and through a stable and efficient data validation model inverse Q filtering application results meanwhile summarizes the stable inverse Q filtering characteristics. Finally, the algorithm with a conventional inverse Q filtering were compared, and eventually applied to the actual seismic data.


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