On the assumption that the wavelet is causal and nonminimum phase, an autoregressive moving average （ARMA） model is introduced to fit the seismic trace. Seismic wavelet extraction is converted to parameters estimati...On the assumption that the wavelet is causal and nonminimum phase, an autoregressive moving average （ARMA） model is introduced to fit the seismic trace. Seismic wavelet extraction is converted to parameters estimation of the ARMA model. Singular value decomposition （SVD） of an appropriate matrix formed by autocorrelation is exploited to determine the autoregressive （AR） order, and the cumulant-based SVD-TLS （total least squares） approach is proposed to obtain the AR parameters. The author proposes a new moving average （MA） model order determination method via combining the information theoretic criteria method and higher-order cumulant method. The cumulant approach is used to achieve the MA parameters. Theoretical analysis and numerical simulations demonstrate the feasibility of the wavelet extraction approach.展开更多
The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colore...The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colored noise. An approach combining the combined information theoretic criteria and eigen- value correction, is presented to determine number of signals. The method uses maximum likelihood （ML） and information theoretic criteria to estimate coherent signals alternately, then eliminate the inequality of the eigenvalues caused by colored noise by correcting the noise eigenvalues. The computer simulation results prove the effective performance of the method.展开更多
基金supported by the National High Technology Research and Development Program of China (863 Program, No.2007AA09Z301)the Graduate Innovation Fund of China University of Petroleum and National Natural Science Foundation of China (40974072)
文摘On the assumption that the wavelet is causal and nonminimum phase, an autoregressive moving average （ARMA） model is introduced to fit the seismic trace. Seismic wavelet extraction is converted to parameters estimation of the ARMA model. Singular value decomposition （SVD） of an appropriate matrix formed by autocorrelation is exploited to determine the autoregressive （AR） order, and the cumulant-based SVD-TLS （total least squares） approach is proposed to obtain the AR parameters. The author proposes a new moving average （MA） model order determination method via combining the information theoretic criteria method and higher-order cumulant method. The cumulant approach is used to achieve the MA parameters. Theoretical analysis and numerical simulations demonstrate the feasibility of the wavelet extraction approach.
文摘The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colored noise. An approach combining the combined information theoretic criteria and eigen- value correction, is presented to determine number of signals. The method uses maximum likelihood （ML） and information theoretic criteria to estimate coherent signals alternately, then eliminate the inequality of the eigenvalues caused by colored noise by correcting the noise eigenvalues. The computer simulation results prove the effective performance of the method.
