High-order Statistics Based Realization Of Seismic Relecticity And Wavelet

Both the wavelet estimation and the seismic deconvolution are studied in this paper. The wavelet estimation and seismic deconvolution are always based on the assumpsition of Guassality and whiten noise to relecticity and minimum phase to the seismic wavelet. It commonly has a good effect in the practical application, but it can't be sure that these assumpsitions are always accurate. However, the wavelet estimation based on the High-order statistic can eliminate the assumpsition of Guassality and whiten noise to relecticity and minimum phase to the seismic wavelet. Furthermore, the relecticity can be separated. Then the seismic deconvolution can be achieved. The paper studies the non-minimum phase seismic wavelet estimation and seismic deconvolution based on the production of fore people. At the same time, applying independent component anaysis to the blind deconvolution of seismic data in a creative way. Completed primarily below work:1. Neglecting noise, achieves the minimum phase seismic wavelet estimation and seismic deconvolution.2. Neglecting noise, applying bispectrum to recover the no minimum wavelet, and then applying the homomorphic deconvolution method to realize the deconvolution to obtain the relecticity.3. Neglecting noise, making use of time lagged version matrix of convolved signal and seismic wavelet banded convolving mixture matrix to construct a basic ICA model. By applying FastICA algorithm, and combining the banded property as a prior information, giving rised to a banded ICA algorithm(B-ICA), more reflectivity series are produces as many as the dimension of the seismic wavelet filter, and finally one best independent component can be extracted from the candinate solutions by additional information from the seismic convolution model.4. Neglecting noise, changing the seismic record from time realm to complex cepstrum realm to transform the common seismic model to the basic ICA model. By applying the FastICA algorithm, separating the the seismic wavelet and reflectivity and changing the result back to the time realm.The model and real seismic data mumerical examples all shows that the stasticstical deconvolution based on ICA can inverse blindly the wavelet and the reflectivity at the same time with no assumpsition of Guassality and whiten noise to relecticity, and no minimum phase to seismic wavelet. The algorithms based on ICA refered here can slove the seismic signals blind deconvolution effectively and worth doing more researchs.