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随机地震反演技术是将地质统计理论和地震反演相结合的反演方法,它将地震资料、测井资料和地质统计学信息融合为地下模型的后验概率分布,利用马尔科夫链蒙特卡洛(MCMC)方法对该后验概率分布采样,通过综合分析多个采样结果来研究后验概率分布的性质,进而认识地下情况。本文首先介绍了随机地震反演的原理,然后对影响随机地震反演效果的四个关键参数,即地震资料信噪比、变差函数、后验概率分布的样本个数和井网密度进行分析并给出其优化原则。资料分析表明地震资料信噪比控制地震资料和地质统计规律对反演结果的约束程度,变差函数影响反演结果的平滑程度,后验概率分布的样本个数决定样本统计特征的可靠性,而参与反演的井网密度则影响反演的不确定性。最后通过对比试验工区随机地震反演和基于模型的确定性地震反演结果,指出随机地震反演可以给出更符合地下实际情况的模型。
Stochastic seismic inversion is an inversion method that combines the theory of geostatistics and seismic inversion. It combines the seismic data, logging data and geostatistics information into the posterior probability distribution of the underground model, and uses the Markov chain Monte-Carlo Carlo (MCMC) method is used to sample the posterior probability distribution, and the properties of a posteriori probability distribution are studied through comprehensive analysis of multiple sampling results to further understand the subsurface conditions. In this paper, the principle of stochastic seismic inversion is introduced firstly. Then, the four key parameters that affect the stochastic seismic inversion, namely, the signal-to-noise ratio and variogram of seismic data, the number of samples with posterior probability distribution and the density of well pattern are analyzed And give its optimization principle. The data analysis shows that the SNR of seismic data controls the constraint degree of seismic data and geostatistics on the inversion results. The variogram affects the smoothness of inversion results. The number of samples posterior probability distribution determines the reliability of the statistical characteristics of samples. The well density involved in the inversion affects the uncertainty of the inversion. Finally, by comparing the results of stochastic seismic inversion and model-based deterministic seismic inversion, it is pointed out that stochastic seismic inversion can give a model that is more in line with the actual situation in the underground.