Reduction Methods of Seism Data Random Noise Based on HigherOrder Statistics 

Author  ZhangWenXue 
Tutor  WangZhongRen 
School  Jilin University 
Course  Applied Mathematics 
Keywords  higherorder statistics bispectra Yule—Walker of equation dual partition the Finite Volume Method 
CLC  P315.63 
Type  Master's thesis 
Year  2007 
Downloads  224 
Quotes  0 
Random noise is unavoidable in seismic exploration.In the random noise reduction,the method of prognose filter in spacefrequency was initiated by Luis Canales in 1984. The method firstly commits a Fourier transform to each of the seism data in one of the time windouw. Swing energy of signal is predictalbe for every frequency. It is the linear combination of border upon seism The distribution of noise energy is random. So, in the light of least mean square error,a prognose error filter is determined, the output is the estimate of the unpredictalbe portion from each of primal seism. The output of signals can be obtained by detracting the prediction error from each of primal seism, again commits a inverse Fourier transform to each of primal seism,it find prognose output of signals.M.K.Chase extend fx space diviable filter to 3Din 1992 ,it come true diviable filter which 3D form of 3D seismic data.ZhongRen.Wang uinged 2D AR model to reducte random noise in tx,y space.The most methods mentioned above based on gaussian white noise are limited, because, A great many noise generally is gaussian colored noise or nongaussian colored noise.The main mathematic tool in treating nonminimum phase signals,nonlinear signals and nonGanssian signals is higherorder cumulants or higherorder spectra. During 1960s, researchers in mathematics, statistics, hydrokinetics, signal treating and other fields began to research higherorder cumulants. But it is fully developing after 1980s, After a few yeas research, higherorder cumulants is obtained lots of application in radar, sonar, communication, oceanography, astronomy, electromagnetism, plasma,physical geography, biomedicine and other fielde. Higherorder spectra have been given lots of attention lately due to their ability to preserve information of nonGaussian stationary random processes. Higherorder cumulants have more advantage than correlation function. Hence,all the signals treated by correlation without satisfied results can be treated by higherorder cumnlants.Swami A and Mendel J M are firstly lodge modified YuleWalker equations[16] based on higherorder statistics(HOS),threeorder comulant is zero for gaussian white noise.so it able to reduce the noise.This article research reduce random noise in data of seism based on higherorder statistics,at first using conventional time sequence methods reduce random noise, withal the article advance news YuleWalker equation based on threeorder moment,using the equation to foundation AR model to reduce random noise, lastly the article reduce random noise based on bispectra YuleWalker equations.above arithmetic all is implemented in MATLAB6.5.This article first commits a fourier transform to every seism data with random noise. withal Foundation AR models for every row, withal, Make certain order and quotiety of AR models,comeback aboriginal signals by the AR models.so inverse fourier transform to every seism data,gained reduced signals.The article time after time experimentation order of AR model for conventional time sequence methods and mend YuleWalker equation methods,attain optimal effect.It explain the two method which agility and operable.when there is no input of unconventional wave, this article gained the following conclusionslf noise is gaussian white noise,all methods able reduce random noise,conventional time sequence methods is good than modified YuleWalker equation and mend YuleWalker equation methods ;If noise is gaussiancolored noise,modified YuleWalker equation is had best,conventional time sequence methods is flooey;If noise is nongaussiancolored noise,conventional time sequence methods unable reduce random noise,modified YuleWalker equation is had best,but time is long.