【摘要】 基于Metropolis抽样的非线性反演应用贝叶斯理论框架,是一种基于蒙特卡洛的非线性反演方法,能够有效地融合测井资料中的高频信息,提高反演结果的分辨率。首先通过快速傅里叶滑动平均模拟算法(FFTMA)和逐渐变形算法(GDM)得到基于地质统计学的先验信息;进而构建似然函数;最后利用Metropolis算法对后验概率密度进行抽样,得到反演问题的解。其中FFT-MA模拟作为一种高效的频率域模拟方法,融入GDM更新算法之后,可以在保持模拟空间结构不变的前提下,连续修改储层模型,保证反演结果有效地收敛,直至满足实际观测地震记录。模型试算和实际数据处理结果表明:基于Metropolis抽样的非线性反演可以提供合理的弹性参数信息,尤其是提高纵波速度的分辨率,即使信噪比较小时,仍然可以反演出合理的弹性参数信息,从而证明了该方法的有效性;当不考虑噪声时,纵、横波阻抗的反演分辨率较弹性参数本身的反演分辨率更高。更多还原

【Abstract】 Nonlinear inversion based on Metropolis sampling algorithm is formulated in the Bayesian framework.As one kind of Monte Carl non-linear inversions,it can effectively integrate high frequency information of well logging data,and obtain inversion results with a higher resolution.Firstly,we get the priori information through fast Fourier transform moving average(FFT-MA)and gradual deformation method(GDM).Second,we structure likelihood function.Then we apply Metropolis algorithm in order to obtain an exhaustive characterization of the posteriori probability density.FFT-MA is a kind of efficient simulation method.Combined with GDM,it can constantly modify reservoir model and keep the spatial structure unchanged until it matches the observed seismic data.According to the model trial and real data processing,we can conclude that nonlinear inversion based on Metropolis sampling algorithm provide reasonable elastic parameter information,especially it improves the resolution of P-wave velocity.Even when the signal noise ratio(SNR)is relatively low,it can still show reasonable elastic parameter information,which proves the effectiveness of the proposed method.The inversion resolution of Pwave and S-wave impedances is higher than elastic parameters inversion if we do not consider the noise.更多还原