摘要
Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.
AVO反演是获取地下介质弹性参数的重要手段。反演可在时间域或频率域实现,时间域反演稳定性好但分辨能力受限;频率域反演受益于有利频带的选择,分辨能力提高但反演结果容易受噪音影响。在贝叶斯反演理论框架下,提出了一种时间域和频率域联合的AVO反演方法。该方法在反演目标函数构建中融合了时间域和频率域信息的影响,假设待反演参数先验信息和似然函数分别服从柯西和高斯分布,考虑了待反演参数间的相关性,并采用模型约束提高了反演稳定性。模型测试表明,利用时频域联合反演得到的纵横波速度等弹性参数反射系数的频谱带宽要优于仅用时间域信息得到的结果,且合成地震记录信噪比为2时,仍可以得到较好的反演结果,验证了方法在保持稳定性的同时改善了分辨能力的优势。实际资料试处理进一步验证了方法的可行性及其相对单纯时间域或频率域反演的优势。
基金
supported by the National Nature Science Foundation Project(Nos.41604101 and U1562215)
the National Grand Project for Science and Technology(No.2016ZX05024-004)
the Natural Science Foundation of Shandong(No.BS2014NJ005)
Science Foundation from SINOPEC Key Laboratory of Geophysics(No.33550006-15-FW2099-0027)
the Fundamental Research Funds for the Central Universities
