摘要
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.
基于Cauchy先验分布的贝叶斯AVO反射率反演,可以产生类似于稀疏脉冲反演的结果,增强了对大反射系数的识别能力,反演过程中正则项和参数矩阵的计算都需要用到由上一步迭代所估计出的模型参数信息,因而反问题呈现非线性,此外该过程依赖于模型参数之间的线性统计关系,而且反演结果为反射率而非弹性参数,不利于储层预测和流体检测。贝叶斯AVO波形反演通过将反射率改写为弹性参数差分形式,直接从叠前地震数据中提取弹性参数,无需对弹性参数之间关系做线性假设。本文综合考虑上述两种方法的优点,在研究过程中仍然采用Cauchy先验分布,同时对贝叶斯AVO反射率的反演过程进行了修改,实现了基于Cauchy先验分布的AVO弹性参数弱非线性波形反演。本方法的提出有效避免了模型参数之间的线性假设,同时也能够从地震数据中直接反演得到纵、横波速度和密度。理论合成数据实验证明,此方法可以直接从叠前地震数据中提取弹性参数,而且在含有噪音的情况下也能得到比较准确的反演结果。
基金
supported by the National High-Tech Research and Development Program of China(863 Program)(No.2008AA093001)
