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The Stable Inverse Q Filtering Method Based On Adaptive Optimization Of Q Value

Posted on:2018-04-25Degree:MasterType:Thesis
Country:ChinaCandidate:J X ShenFull Text:PDF
GTID:2310330512497324Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
Because of the existence of viscous absorption,the small scale inhomogeneity of the earth medium also produces a magnitude attenuation effect similar to that of viscous absorption,The objectives above existence leads to the attenuation of the amplitude of seismic wave propagation.In the oil field exploration and development period,how to improve the exploration ability of subtle oil and gas reservoirs,such as deep thin sand body,thin interbed and micro amplitude structure and subtle oil and gas reservoirs,and how to enhance the identification ability of oil and gas recovery systems such as small faults and fracture zones,then identify favorable oil and gas reservoirs.All the above problems are the main targets of seismic exploration.So it is necessary to compensate and correct the absorption and dispersion of viscous medium.Among all the methods,inverse Q filtering is a kind of commonly used compensation method.The conventional inverse Q filtering method is based on wave field continuation theory.so it always has the disadvantages of instability or amplitude compensation.The compensation of high frequency energy will inevitably increase the high frequency noise and with instability phenomenon.The amplitude of the seismic data can be controlled by the inverse Q filter amplitude compensation,which is usually constrained by the time constant gain limit,but it often suppresses the high frequency components of the deep seismic wave and reduces the resolution of the deep seismic data.Therefore,in view of the above two problems,on the basis of previous studies,this paper puts forward two new methods of stability control,which are the quadratic function method and the trigonometric function method.In this paper,two kinds of new anti Q filter compensation operators are designed,and the validity of the model is validated by using the theoretical model.The analysis results show that the stability of 2 kinds of new control methods can effectively enhance the stability of inverse Q filtering process,and compared to the amplitude stability factor method has better recovery effect,more significantly improve deep resolution.On the basis of these two methods,the adaptive gain limit is combined with the stability control method,so that the gain limit of the optimal amplitude compensation function is adaptive to the cutoff frequency of the effective band of seismic data,The compensation effect of time domain and frequency domain is analyzed by theoretical model test,and the compensation effect is improved obviously.It is more difficult to determine the Q value based on the reflection seismic data due to the phenomenon of thin layer tuning.Therefore,on the basis of the above research,this paper is to overcome the phenomenon of thin tuning Q value modeling method research,put forward a set of frequency derivative arithmetic based on mean value effect to eliminate the thin tuning effect,using Q to determine the value of scanning frequency recovery status,so as to realize the Q estimation method of equivalent value by reflection data;By changing the data obtained from seismic attenuation of superposition of Q value idea,put forward the first compensation viscous migration of seismic data,the seismic wave energy loss compensation,the compensation data show no attenuation characteristics of viscosity.After eliminating the seismic data after a certain distance,the amplitude is too low to be affected by the background noise and multiple waves.The essence of the method is to select the correct superposition of the Q value to make the spectrum spectrum of the deep and shallow stratum wavelet consistent.The application of the Q modeling method and the theoretical model and the actual seismic data processing proves its practicability.
Keywords/Search Tags:Inverse Q filtering, Stability control, Q value modeling, Thin tuning
PDF Full Text Request
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