The Modeling And Extraction Research For High Resolution Seismic Wavelet Estimation

The accurate estimation of the seismic wavelet has profound significance for seismic data processing in the sense of high-resolution, high signal-to-noise ratio and high fidelity. Recently, the statistical methods of wavelet extraction achieved comprehensive application in real seismic data processing. The thesis thoroughly studied the accurate model of the seismic trace and the wavelet extraction methods based on high-order statistics.In practice, the real seismic wavelet is noncause and mixed-phase. Based on the convolution model, both the MA (moving average) and ARMA (autoregressive moving average) models were introduced to fit the seismic trace. Then the cumulant-based matrix-equation approach and cumulant matching method were employed to estimate the wavelet and evaluate the applicability of each model. The simulations and real seismic data experiments demonstrate that the ARMA model provides a parsimonious, more efficient signal modeling in fitting seismic trace than the MA model does, and the cumulant-based methods not only retain the amplitude, frequency and phase information of the seismic data, but also effectively eliminate the colored Gaussian noise and obtain a preferable wavelet with high efficiency.Combining the matrix-equations algorithm and the matching algorithm, a new seismic wavelet extraction method was proposed. The cumulant-based matrix-equations algorithm is a linear wavelet extraction method. When the amount of trace data is finite and even insufficient, the estimated wavelet always has remarkable estimated error and variance. As for the cumulant matching method, the veracity of the initial solution range directly influences the optimization efficiency of the method. Therefore, the combined method first extracted a inferior wavelet estimation by the cumulant-based linear-equations method, then relied on it to fix the initial solution range, finally obtained a fine wavelet via the cumulant matching method. The combined method synthetizes the merits of two wavelet extraction methods, thus improves the whole computational efficiency.Moreover, the thesis ameliorated the nonlinear optimization algorithm of the cumulant matching method for seismic wavelet extraction. The combination of The Adaptive Immune-Genetic Algorithm and the small neighbourhood searching algorithm improves the whole computational efficiency and precision.Through the application of the new techniques, the precision and efficiency of the seismic wavelet extraction gain obvious enhancement. The achievements indicate the broad application future of high-order statistics in the field of the seismic data processing.