Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized...Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.展开更多
提出了一种新的自适应预测反卷积的方法:基于知识的自适应滤波(NBA,knowledge based Adaptive filter).采用该法可以将先验的波形知识与自适应滤波技术相结合,实现缓变系统的自适应反卷积.与普通自适应预测反卷积相比,NBA方法由于加入...提出了一种新的自适应预测反卷积的方法:基于知识的自适应滤波(NBA,knowledge based Adaptive filter).采用该法可以将先验的波形知识与自适应滤波技术相结合,实现缓变系统的自适应反卷积.与普通自适应预测反卷积相比,NBA方法由于加入了先验的波形知识,从而在输出波形失真误差上及自适应学习速度上均有很大的改善,特别是该方法不再限于仅可对白噪与线性系统的卷积才可进行反卷积的理论限制,提出了一种非白化滤波的自适应反卷积技术.本文从原理上及计算机仿真结果上证实了NBA法的有效性.展开更多
基金supported by the National High-Tech Research and Development Program of China(863 Program)(No.2008AA093001)
文摘Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.
文摘提出了一种新的自适应预测反卷积的方法:基于知识的自适应滤波(NBA,knowledge based Adaptive filter).采用该法可以将先验的波形知识与自适应滤波技术相结合,实现缓变系统的自适应反卷积.与普通自适应预测反卷积相比,NBA方法由于加入了先验的波形知识,从而在输出波形失真误差上及自适应学习速度上均有很大的改善,特别是该方法不再限于仅可对白噪与线性系统的卷积才可进行反卷积的理论限制,提出了一种非白化滤波的自适应反卷积技术.本文从原理上及计算机仿真结果上证实了NBA法的有效性.
