Seismic fluid identification using a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo method

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.

SEMI-BLIND CHANNEL ESTIMATION OF MULTIPLE-INPUT/MULTIPLE-OUTPUT SYSTEMS BASED ON MARKOV CHAIN MONTE CARLO METHODS

This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.

Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

Haze concentration prediction,especially PM2.5,has always been a significant focus of air quality research,which is necessary to start a deep study.Aimed at predicting the monthly average concentration of PM2.5 in Beijing,a novel method based on Monte Carlo model is conducted.In order to fully exploit the value of PM2.5 data,we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively.The results show that these data are both approximately normal distribution.On the basis of the results,a Monte Carlo method can be applied to establish a probability model of normal distribution based on two different variables and random sampling numbers can also be generated by computer.Through a large number of simulation experiments,the average monthly concentration of PM2.5 in Beijing and the general trend of PM2.5 can be obtained.By comparing the errors between the real data and the predicted data,the Monte Carlo method is reliable in predicting the PM2.5 monthly mean concentration in the area.This study also provides a feasible method that may be applied in other studies to predict other pollutants with large scale time series data.

Study of photometric phase curve:assuming a cellinoid ellipsoid shape for asteroid(106)Dione

We carried out new photometric observations of asteroid （106） Dione at three apparitions （2004, 2012 and 2015） to understand its basic physical properties. Based on a new brightness model, new photometric observational data and published data of （106） Dione were analyzed to characterize the morphology of Dione＇s photometric phase curve. In this brightness model, a cellinoid ellipsoid shape and three-parameter （H, G1, G2） magnitude phase function system were involved. Such a model can not only solve the phase function system parameters of （106） Dione by considering an asymmetric shape of an asteroid, but also can be applied to more asteroids, especially those without enough photometric data to solve the convex shape. Using a Markov Chain Monte Carlo （MCMC） method, Dione＇s absolute magnitude of H = 7.66＋0.03-0.03 mag, and phase function parameters G1 = 0.682＋0.077-0.077 and G2 = 0.081＋0.042-0.042 were obtained. Simultaneously, Dione＇s simplistic shape, orientation of pole and rotation period were also determined preliminarily.

On Stochastic Error and Computational Efficiency of the Markov Chain Monte Carlo Method

InMarkov ChainMonte Carlo(MCMC)simulations,thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples.These samples are selected in accordance with the probability distribution function,known from the partition function of equilibrium state.As the stochastic error of the simulation results is significant,it is desirable to understand the variance of the estimation by ensemble average,which depends on the sample size(i.e.,the total number of samples in the set)and the sampling interval(i.e.,cycle number between two consecutive samples).Although large sample sizes reduce the variance,they increase the computational cost of the simulation.For a given CPU time,the sample size can be reduced greatly by increasing the sampling interval,while having the corresponding increase in variance be negligible if the original sampling interval is very small.In this work,we report a few general rules that relate the variance with the sample size and the sampling interval.These results are observed and confirmed numerically.These variance rules are derived for theMCMCmethod but are also valid for the correlated samples obtained using other Monte Carlo methods.The main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them.

Multilevel Markov Chain Monte Carlo Method for High-Contrast Single-Phase Flow Problems

In this paper we propose a general framework for the uncertainty quantification of quantities of interest for high-contrast single-phase flow problems.It is based on the generalized multiscale finite element method(GMsFEM)and multilevel Monte Carlo(MLMC)methods.The former provides a hierarchy of approximations of different resolution,whereas the latter gives an efficient way to estimate quantities of interest using samples on different levels.The number of basis functions in the online GMsFEM stage can be varied to determine the solution resolution and the computational cost,and to efficiently generate samples at different levels.In particular,it is cheap to generate samples on coarse grids but with low resolution,and it is expensive to generate samples on fine grids with high accuracy.By suitably choosing the number of samples at different levels,one can leverage the expensive computation in larger fine-grid spaces toward smaller coarse-grid spaces,while retaining the accuracy of the final Monte Carlo estimate.Further,we describe a multilevel Markov chain Monte Carlo method,which sequentially screens the proposal with different levels of approximations and reduces the number of evaluations required on fine grids,while combining the samples at different levels to arrive at an accurate estimate.The framework seamlessly integrates the multiscale features of the GMsFEM with the multilevel feature of the MLMC methods following the work in[26],and our numerical experiments illustrate its efficiency and accuracy in comparison with standard Monte Carlo estimates.

Monte Carlo Simulations of Density Profiles for Hard-Sphere Chain Fluids Confined Between Surfaces

Covering a wide range of bulk densities, density profiles for hard-sphere chain fluids (HSCFs) with chain length of 3,4,8,20,32 and 64 confined between two surfaces were obtained by Monte Carlo simulations using extended continuum configurational-bias (ECCB) method. It is shown that the enrichment of beads near surfaces is happened at high densities due to the bulk packing effect, on the contrary, the depletion is revealed at low densities owing to the configurational entropic contribution. Comparisons with those calculated by density functional theory presented by Cai et al. indicate that the agreement between simulations and predictions is good. Compressibility factors of bulk HSCFs calculated using volume fractions at surfaces were also used to test the reliability of various equations of state of HSCFs by different authors.

基于Monte-Carlo法的异形双层连续梁桥地震易损性分析

地震灾害对桥梁安全具有严重影响,复杂桥梁结构的地震易损性研究尤为重要。本文基于OpenSees有限元软件,建立了双层异形连续梁桥的有限元模型,应用Monte-Carlo方法进行了桥梁系统易损性分析,选取支座、桥墩和圆支架作为结构易损性构件。根据所选的地震动强度参数以及各易损性构件的损伤指标进行了非线性时程分析,获得的易损性曲线介于一阶界限法串联系统的上下界之间,验证了Monte-Carlo法的有效性。与Monte-Carlo法双层桥梁系统易损性曲线进行了对比,发现上层桥梁系统易损性曲线与双层桥梁系统易损性曲线在各个损伤状态下相差很小。对于双层桥梁系统易损性的分析,可简化为上层桥梁系统易损性分析。

On Finding the Smallest Generalized Eigenpair Using Markov Chain Monte Carlo Algorithm

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.

端点附壁的高分子链形状的MonteCarlo模拟

分别基于简立方格点和四面体格点模型对一端吸附在无限大平面的高分子链 (平面接枝高分子链 )的形状进行了 Monte Carlo摸拟 .结果表明 ,接枝高分子链的形状更偏离球形 ,〈L1 2〉∶〈L2 2〉∶〈L32〉的极限值约为 1∶ 2 .75∶ 1 2 .5,其中〈L1 2 〉,〈L2 2 〉和〈L32 〉分别为回转半径张量的本征值 L1 2 ,L2 2 和 L32 ( L1 2 <L2 2 <L32 )的统计平均 ;链长相同时 ,接枝高分子链的比值〈L2 2 〉/〈L1 2 〉和〈L32 〉/〈L1 2 〉均比自由 (稀溶液中 )高分子链的大 ,且与高分子链的格点模型有关 .

用 Monte Carlo 方法计算面源出射重离子在圆柱形微观体中的弦长分布

利用ＭｏｎｔｅＣａｒｌｏ方法，计算了在不同的几何条件下平面放射源出射的重离子在圆柱体中的弦长的概率密度（即弦长分布函数）。计算结果表明，弦长分布函数及平均弦长与具体的几何条件是有关的，当面源的半径增大或放射源到灵敏体的距离增大时，平均弦长的值减小。

基于Monte Carlo方法的一对二马尔可夫随机格斗模型

讨论了一对二马尔可夫随机格斗双方获胜概率计算问题。提出了一种新颖的一对二马尔可夫随机格斗任意对抗回合双方获胜概率的计算方法,该方法首先基于Monte Carlo仿真计算各个对抗回合中双方发射次序的概率分布,再利用全概率公式确定马尔可夫链的状态转移概率矩阵,从而克服了马尔可夫随机格斗模型往往只能提供无限对抗回合之后格斗双方获胜概率的缺点,为运用马尔可夫随机格斗研究火力运用和弹药分配提供了新途径,并用实例说明了该方法的有效性。

聚硅烷链形状的 Monte Carlo 研究

用ＭｏｎｔｅＣａｒｌｏ方法对θ溶液中考虑二级相互作用的聚硅烷链的形状进行了研究。结果表明聚硅烷链的形状明显偏离球形，它与链长有关，长链极限的比值＜Ｌ２１＞∶＜Ｌ２２＞∶＜Ｌ２３＞约为１∶２．７∶１２．１。还发现聚硅烷链转动惯量的最长主轴与末端距矢量之间夹角的统计平均值＜θ＞的极限值约为２７°。

基于网络排队模型的Monte Carlo多线程电梯交通流优化设计

采用Monte Carlo方法对串并联结合的复合电梯系统进行了分析,应用多线程技术模拟电梯交通流模型,并给出相应的算法流程。将该算法应用到电梯配置测评中,通过对多个仿真实例的比较,根据配置结构,给出各实例的相应性能指标。结果表明,以本模型为基础建立的电梯配置测评系统可以平衡乘客候梯时间和电梯负载之间的关系,对电梯系统的结构配置给出合理建议,证明了Monte Carlo方法在电梯群控系统测评和优化中的可行性和优越性。

Monte-Carlo方法在尺寸链方程组计算中的应用

为解决引信产品设计过程中的某些复杂尺寸链无法进行极大极小值计算的问题,分析和对比了尺寸链计算的主要方法,并将Monte-Carlo方法应用到实际的复杂尺寸链方程组计算问题中,得到的结果与实际结果相符合。Monte-Carlo方法可应用于复杂的尺寸链计算问题中,并可结合MATLAB数值计算方法求解组成环除正态分布外的其他分布规律的尺寸链问题。

自旋S=1/2号反铁磁链的量子Monte Carlo研究

在投影量子Monte Carll方法基础上,提出了一种有效地处理反铁磁自旋长链的Monte Carlo方法。用该算法计算了自旋S=1/2、链长N=32的反铁磁链的基态能。计算结果与Betsuyaku和Bonner等人的结果相符合。同时,计算了反铁磁链的基态关联函数,得出了随链长N的增加,其自旋链的长程有序消失的结论。

用Monte Carlo方法研究聚亚甲基长链的统计性质

用Monte Carlo方法研究聚亚甲基长链的统计性质,如:平均分布几率P-(r)、每一个键的平均能量、均方末端距〈R2〉等.然后,将这些结果和完全统计方法获得的结果进行对比,两者是相似的,而Monte Carlo方法有计算量小的优势.

SHAPE OF POLYMER CHAINS ON A TETRAHEDRAL LATTICE

The shape of unperturbed polymer chains was studied using the Monte Carlo technique on a tetrahedral lattice. The asphericity A, the ratios <L-2(2)>/<L-1(2)> and <L-3(2)>/<L-1(2)> were calculated for different Values of polymer chain length n, conformational energy epsilon (epsilon greater than or equal to 0) and temperature T. The asphericity A decreases with the increase of chain length and tends to reach its limiting value rapidly with the decrease of gamma (gamma = epsilon/k(B)T). For large n, A is about 0.525 +/- 0.005, the ratios <L-2(2)>/<L-1(2)> and <L-3(2)>/<L-1(2)> are about 2.7 and 12.0, respectively, and are almost independent of gamma, but for short chains, they depend on gamma.

自旋S=1/2反铁磁链元激发谱的量子Monte Carlo研究

提出一种扩展投影量子Monte Carlo方法。用该方法计算了S=1/2反铁磁自旋链(链长N=32)的元激发谱。计算结果表明元激发谱为E_k=C|sinK|,与Anderson用自旋波理论求得的E_k=|sinK|不同。它存在一个振幅修正参数C。

FAST CONVERGENT MONTE CARLO RECEIVER FOR OFDM SYSTEMS

The paper investigates the problem of the design of an optimal Orthogonal Fre- quency Division Multiplexing (OFDM) receiver against unknown frequency selective fading. A fast convergent Monte Carlo receiver is proposed. In the proposed method, the Markov Chain Monte Carlo (MCMC) methods are employed for the blind Bayesian detection without channel es- timation. Meanwhile, with the exploitation of the characteristics of OFDM systems, two methods are employed to improve the convergence rate and enhance the efficiency of MCMC algorithms. One is the integration of the posterior distribution function with respect to the associated channel parameters, which is involved in the derivation of the objective distribution function; the other is the intra-symbol differential coding for the elimination of the bimodality problem resulting from the presence of unknown fading channels. Moreover, no matrix inversion is needed with the use of the orthogonality property of OFDM modulation and hence the computational load is significantly reduced. Computer simulation results show the effectiveness of the fast convergent Monte Carlo receiver.