This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method...This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.展开更多
We introduce the potential-decomposition strategy (PDS), which can be used in Markov chain Monte Carlosampling algorithms.PDS can be designed to make particles move in a modified potential that favors diffusion in pha...We introduce the potential-decomposition strategy (PDS), which can be used in Markov chain Monte Carlosampling algorithms.PDS can be designed to make particles move in a modified potential that favors diffusion in phasespace, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner.Furthermore,if the accepted trial samples are insufficient, they can be recycled as initial states to form more unbiased samples.Thisstrategy can greatly improve efficiency when the original potential has multiple metastable states separated by largebarriers.We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating inthese two representative examples that convergence is accelerated by orders of magnitude.展开更多
Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain M...Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo （MCMC） method is proposed in this paper, combining conventional MCMC method based on global optimization with a preconditioned conjugate gradient （PCG） algorithm based on local optimization, so this method does not depend strongly on the initial model. It converges to the global optimum quickly and efficiently on the condition that effi- ciency and stability of inversion are both taken into consid- eration at the same time. The test data verify the feasibility and robustness of the method, and based on this method, we extract the effective pore-fluid bulk modulus, which is applied to reservoir fluid identification and detection, and consequently, a better result has been achieved.展开更多
地震随机反演方法由于井间数据缺失,反演结果的横向连续性较差。且反演效率低、反演结果随机性强。为此,我们提出基于地震波形约束的地质统计学反演方法。用地震数据的相关系数来衡量地震波形的相似程度,代替传统的变差函数进行序贯高...地震随机反演方法由于井间数据缺失,反演结果的横向连续性较差。且反演效率低、反演结果随机性强。为此,我们提出基于地震波形约束的地质统计学反演方法。用地震数据的相关系数来衡量地震波形的相似程度,代替传统的变差函数进行序贯高斯模拟。在贝叶斯框架下,结合地震数据的约束,利用马尔科夫链-蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法对模拟结果进行随机扰动和全局寻优,获得优化的参数反演结果。模型测试结果表明,基于地震波形约束的初始模型较为精确地刻画了地下储层的空间结构。对其进行迭代优化可以加快马尔科夫链的收敛速度,有效提高反演结果的精度。本文将提出的地质统计学反演方法用于某油田实际地震数据,在随机模拟过程和目标函数的约束中,充分挖掘了地震波形蕴含的地质信息,并为实现多数据联合约束地震反演提供了理论依据。展开更多
提出将贝叶斯统计推断方法推广应用于大气紊流激励下飞行器结构的颤振分析,对含不确定性因素影响的模态参数识别与颤振边界预测进行研究。在采用自然激励技术从结构在大气紊流激励下的响应中提取自由衰减信号后,基于贝叶斯统计推断,通...提出将贝叶斯统计推断方法推广应用于大气紊流激励下飞行器结构的颤振分析,对含不确定性因素影响的模态参数识别与颤振边界预测进行研究。在采用自然激励技术从结构在大气紊流激励下的响应中提取自由衰减信号后,基于贝叶斯统计推断,通过马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)算法对结构模态参数的后验概率密度函数进行采样识别,并利用Z-W(Zimmerman-Weissenburger)颤振裕度法获取颤振速度概率分布,预测颤振边界并分析其不确定性。进行了数值仿真研究,对大气紊流激励下的结构响应数据进行分析,验证了所提出方法的有效性。展开更多
针对响应服从非正态分布和模型不确定性的稳健参数设计问题,在Polya树混合建模的框架下,构建了一种半参数分层贝叶斯响应曲面模型,并在此基础上实现了稳健参数设计。首先,建立贝叶斯半参数模型,并获得模型各参数的后验分布;其次,运用马...针对响应服从非正态分布和模型不确定性的稳健参数设计问题,在Polya树混合建模的框架下,构建了一种半参数分层贝叶斯响应曲面模型,并在此基础上实现了稳健参数设计。首先,建立贝叶斯半参数模型,并获得模型各参数的后验分布;其次,运用马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)算法获得各参数的估计值;然后,基于此构建期望质量损失函数,并利用混合遗传算法全局寻优,获得可控因子的最优设置;最后,通过数值模拟研究和实际案例验证了所提方法的有效性。所提方法能有效解决小样本数据以及模型不确定性对优化结果影响的问题,从而能够获得更稳健可靠的可控因子最优设置。展开更多
文摘This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.
基金Supported by the National Natural Science Foundation of China under Grant Nos.10674016,10875013the Specialized Research Foundation for the Doctoral Program of Higher Education under Grant No.20080027005
文摘We introduce the potential-decomposition strategy (PDS), which can be used in Markov chain Monte Carlosampling algorithms.PDS can be designed to make particles move in a modified potential that favors diffusion in phasespace, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner.Furthermore,if the accepted trial samples are insufficient, they can be recycled as initial states to form more unbiased samples.Thisstrategy can greatly improve efficiency when the original potential has multiple metastable states separated by largebarriers.We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating inthese two representative examples that convergence is accelerated by orders of magnitude.
基金the sponsorship of the National Basic Research Program of China (973 Program,2013CB228604,2014CB239201)the National Oil and Gas Major Projects of China (2011ZX05014-001-010HZ,2011ZX05014-001-006-XY570) for their funding of this research
文摘Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo （MCMC） method is proposed in this paper, combining conventional MCMC method based on global optimization with a preconditioned conjugate gradient （PCG） algorithm based on local optimization, so this method does not depend strongly on the initial model. It converges to the global optimum quickly and efficiently on the condition that effi- ciency and stability of inversion are both taken into consid- eration at the same time. The test data verify the feasibility and robustness of the method, and based on this method, we extract the effective pore-fluid bulk modulus, which is applied to reservoir fluid identification and detection, and consequently, a better result has been achieved.
基金National Natural Science Foundation of China(11161031)Natural Science Foundation of Inner Mongolia(2010MS0116,2009MS0107)Higher School Science and Technology Research Project of Inner Mongolia(NJ10085)~~
基金supported by the National Natural Science Foundation of China[Grant Nos.42174146,42074136,42174144]Innovation Fund Project for Graduate Students of China University of Petroleum(East China)[Grant No.23CX04015A].
文摘地震随机反演方法由于井间数据缺失,反演结果的横向连续性较差。且反演效率低、反演结果随机性强。为此,我们提出基于地震波形约束的地质统计学反演方法。用地震数据的相关系数来衡量地震波形的相似程度,代替传统的变差函数进行序贯高斯模拟。在贝叶斯框架下,结合地震数据的约束,利用马尔科夫链-蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法对模拟结果进行随机扰动和全局寻优,获得优化的参数反演结果。模型测试结果表明,基于地震波形约束的初始模型较为精确地刻画了地下储层的空间结构。对其进行迭代优化可以加快马尔科夫链的收敛速度,有效提高反演结果的精度。本文将提出的地质统计学反演方法用于某油田实际地震数据,在随机模拟过程和目标函数的约束中,充分挖掘了地震波形蕴含的地质信息,并为实现多数据联合约束地震反演提供了理论依据。
文摘提出将贝叶斯统计推断方法推广应用于大气紊流激励下飞行器结构的颤振分析,对含不确定性因素影响的模态参数识别与颤振边界预测进行研究。在采用自然激励技术从结构在大气紊流激励下的响应中提取自由衰减信号后,基于贝叶斯统计推断,通过马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)算法对结构模态参数的后验概率密度函数进行采样识别,并利用Z-W(Zimmerman-Weissenburger)颤振裕度法获取颤振速度概率分布,预测颤振边界并分析其不确定性。进行了数值仿真研究,对大气紊流激励下的结构响应数据进行分析,验证了所提出方法的有效性。
文摘针对响应服从非正态分布和模型不确定性的稳健参数设计问题,在Polya树混合建模的框架下,构建了一种半参数分层贝叶斯响应曲面模型,并在此基础上实现了稳健参数设计。首先,建立贝叶斯半参数模型,并获得模型各参数的后验分布;其次,运用马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)算法获得各参数的估计值;然后,基于此构建期望质量损失函数,并利用混合遗传算法全局寻优,获得可控因子的最优设置;最后,通过数值模拟研究和实际案例验证了所提方法的有效性。所提方法能有效解决小样本数据以及模型不确定性对优化结果影响的问题,从而能够获得更稳健可靠的可控因子最优设置。
