摘要
高斯混合模型(Gaussian Mixture Model,GMM)可以用来描述储层性质的多峰分布特性,多峰特性主要是由于它们在不同离散变量内的变化而引起的.在高斯混合模型中,高斯分量的权值代表离散变量的概率.然而,基于高斯混合模型的贝叶斯线性反演可能会对某些点的离散变量错误地分类,进而影响连续变量的反演结果,尤其存在强噪声的时候.在本文中,我们考虑了离散变量的空间变化性,并将高斯混合模型与序贯指示模拟(Sequential Indicator Simulation,SIS)相结合来确定离散变量的后验条件权值,形成了结合序贯指示模拟的贝叶斯高斯混合线性反演方法.该方法能够准确地对离散变量进行归类,且具有良好的抗噪性.通过模型试算,我们证明了这种方法的可行性,并在实际资料中取得了较好的结果.
In the Bayesian framework,the prior distribution of the model parameters is usually assumed to be Gaussian distribution.However,the model data does not obey the Gaussian distribution in practical applications.Model parameters tend to be multimodal due to different discrete variables,such as facies.The Gaussian Mixture Model can be used to describe the multimodal behavior of model parameters.In the Gaussian Mixture Model,the weights of the Gaussian components represent the probabilities of the discrete variable.However,Bayesian linear inversion based on Gaussian Mixture Model may misclassify discrete variable at some points,which may lead to a bad inversion result of continuous variable,especially when there is a strong noise.In this study,we consider the spatial variability of discrete variable and combine Gaussian Mixture Model with the Sequential Indicator Simulation to determine the posterior conditional weight of each discrete variable,and develop the method of Bayesian Gaussian Mixture linear inversion combined with Sequential Indicator Simulation.This method can accurately classify discrete variable and has good robustness.We certify the feasibility of this method on model data,and then apply this method to actual data with satisfactory results.
作者
印兴耀
贺东阳
宗兆云
李坤
肖张波
张仟策
YIN XingYao;HE DongYang;ZONG ZhaoYun;LI Kun;XIAO ZhangBo;ZHANG QianCe(School of Geosciences,China University of Petroleum,Qingdao Shandong 266580,China;Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao Shandong 266071,China;CNOOC Ltd.Shenzhen Branch,Guangzhou 510240,China;CNPC Logging Liaohe Branch,Panjin Liaoning 124010,China)
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2019年第12期4805-4816,共12页
Chinese Journal of Geophysics
基金
国家自然科学基金(U1562215,U1762103)
国家油气重大专项课题(2016ZX05024004,2017ZX05009001,2017ZX05036005,2017ZX05032003)
中国科学托举人才工程(2017QNRC001)联合资助
