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| Seismic wavelet estimation via a system identification method |
| Shaoshui Wang, Yongshou Dai, Fang Wang |
| College of Information and Control Engineering, China University of Petroleum, Dongying 257061, China |
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Abstract 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.
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Received: 15 April 2009
Published: 10 October 2009
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| Fund: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) |
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Corresponding Authors:
Shaoshui Wang
E-mail: wss.2008@163.com
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