Bayesian Markov chain Monte Carlo inversion for anisotropy of PP-and PS-wave in weakly anisotropic and heterogeneous media

A single set of vertically aligned cracks embedded in a purely isotropic background may be con- sidered as a long-wavelength effective transversely iso- tropy （HTI） medium with a horizontal symmetry axis. The crack-induced HTI anisotropy can be characterized by the weakly anisotropic parameters introduced by Thomsen. The seismic scattering theory can be utilized for the inversion for the anisotropic parameters in weakly aniso- tropic and heterogeneous HTI media. Based on the seismic scattering theory, we first derived the linearized PP- and PS-wave reflection coefficients in terms of P- and S-wave impedances, density as well as three anisotropic parameters in HTI media. Then, we proposed a novel Bayesian Mar- kov chain Monte Carlo inversion method of PP- and PS- wave for six elastic and anisotropic parameters directly. Tests on synthetic azimuthal seismic data contaminated by random errors demonstrated that this method appears more accurate, anti-noise and stable owing to the usage of the constrained PS-wave compared with the standards inver- sion scheme taking only the PP-wave into account.

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.

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.

Markov Chain Monte Carlo Solution of Laplace’s Equation in Axisymmetric Homogeneous Domain

With increasing complexity of today’s electromagnetic problems, the need and opportunity to reduce domain sizes, memory requirement, computational time and possibility of errors abound for symmetric domains. With several competing computational methods in recent times, methods with little or no iterations are generally preferred as they tend to consume less computer memory resources and time. This paper presents the application of simple and efficient Markov Chain Monte Carlo (MCMC) method to the Laplace’s equation in axisymmetric homogeneous domains. Two cases of axisymmetric homogeneous problems are considered. Simulation results for analytical, finite difference and MCMC solutions are reported. The results obtained from the MCMC method agree with analytical and finite difference solutions. However, the MCMC method has the advantage that its implementation is simple and fast.

AN IMPROVED MARKOV CHAIN MONTE CARLO METHOD FOR MIMO ITERATIVE DETECTION AND DECODING

Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability (APP) estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.

基于MCMC的电网安全稳定控制系统动态可靠性评估方法

现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。

Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography

Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.

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。

基于MCMC填补的SSA-SVM煤与瓦斯突出预测模型

为提升煤与瓦斯突出预测准确度,减小数据缺失对煤与瓦斯突出预测的不利影响,提出1种基于链式多重填补马尔科夫链蒙特卡罗(MCMC)的麻雀搜索算法(SSA)优化支持向量机(SVM)预测模型。根据突出影响因素选取模型参数,运用MCMC对突出事故缺失值进行数据填补,采用SSA优化SVM,建立MCMC-SSA-SVM模型对填补后数据集进行预测,验证MCMC填补有效性和SSA优化性能;分别构建SVM、SSA-SVM、PSO-SVM、GAM-SVM、CMC-SVM、MCMC-PSO-SVM和MCMC-GA-SVM这7种模型进行突出预测,对比预测准确度,分析MCMC-SSA-SVM、MCMC-PSO-SVM和MCMC-GA-SVM的适应度。研究结果表明:MCMC填补后准确度均提升7.89个百分点以上,SSA的优化性能强于PSO和GA,MCMC-SSA-SVM预测准确度最高,为97.37%,泛化能力优于对比模型。研究结果可为煤与瓦斯突出预测研究提供借鉴和参考。

随机利率影响下信用风险的Monte-Carlo模拟

在前人利用马尔科夫链表示公司信用等级的基础上,将信用等级和随机利率引入离散时间的信用风险模型中,从而提出随机利率影响下的新的信用风险模型。就上述模型,对不同初始信用等级、初始盈余以及不同时刻的破产概率进行Monte-Carlo模拟,并讨论了相同条件下初始盈余与破产概率、初始信用等级与破产概率以及时间长短与破产概率之间的相互关系。

基于LIM-MCMC模型研究江苏近海北部海域食物网能量流动特征

食物网结构特征和能量流动的研究,对于维持海洋生态系统结构和功能的稳定具有重要意义,有助于深入理解海洋生态系统的复杂过程。本研究基于2019-2021年在江苏近海北部海域开展的季节性渔业资源底拖网调查数据,通过构建基于蒙特卡罗马尔科夫链算法的逆线性模型(Linear Inverse Models using a Monte Carlo Method Coupled with Markov Chain, LIM-MCMC),结合生态网络分析(Ecological Network Analysis,ENA)的方法,分析了该海域生态系统状态和食物网能量流动特征,旨在为江苏近海北部海域食物网营养动力学研究提供参考依据。结果表明,该海域生态系统共包含299条能量流动路径,能量流动分布整体呈典型的金字塔结构,各功能群呼吸消耗和流入有机碎屑的能量保持同步性。通过与其他海域比较发现,江苏近海北部海域生态系统的连接指数(Connectance,C)和系统杂食指数(System Omnivory Index,SOI)分别为0.40和0.22,处于较高水平,表明该生态系统不同营养级间的营养联系较为紧密,食物网结构相对复杂,能够在较大程度上抵御外界扰动。总初级生产力/总呼吸(Total Primary Production/Total Respiration,TPP/TR)和Finn’s循环指数(Finn’s Cycling Index,FCI)分别为1.05和5.76%,表明该生态系统对能量利用效率较高。此外,约束效率(Constraint Efficiency,CE)、发展程度(Extent of Development,AC)、协同效应指数(Synergism Index,b/c)和主导间接效应(Dominance Indirect Effects,i/d)也表明该生态系统具有较高的系统发展程度、再生潜力和系统发展空间。本研究将有助于为江苏近海北部海域生态系统的修复和渔业资源的可持续利用提供理论基础,为实施基于生态系统的渔业管理提供科学依据。

地球物理反演的Monte Carlo解法

本文研究地球物理反演的Monte Carlo解法.文中引入马尔可夫地层沉积模型,改进随机搜索点的产生方法.提出兼用地质知识和地震记录对搜索点进行检验的新方法,有效地提高了Monte Carlo反演方法的计算效率.本文的反演方法除了由约束条件引入确定性地质知识外,还通过马尔可夫沉积模型引入了不确定性地质知识,为地质学与地球物理反演的进一步结合开辟了新途径.

基于MCMC方法的子通道程序模型参数不确定性量化分析研究

假定模型参数的不确定性服从正态分布,根据贝叶斯原理,其最可能的分布是结合先验信息和观测信息得到的最大后验概率,马尔科夫链蒙特卡罗(Markov Chain Monte Carlo,MCMC)抽样适用于此类反问题求解。鉴于随机论方法的巨大计算量,本研究利用BP(Back Propagation)神经网络及相对熵最小化来自适应加密训练数据,从而建立替代复杂正向程序的代理模型,并利用开发的不确定性分析程序对影响空泡份额的模型参数不确定性进行量化分析,选用的子通道程序为COBRA-IV。结果表明:在求得模型参数不确定性后,通过不确定性正向传递得到结果的95%置信区间对实验值的包络性较好,利用不确定性均值对模型进行标定得到的结果较基准值更接近实验值。因此,本研究建立的不确定性量化分析方法能较好适用于子通道程序的不确定性分析。

Comparison of deterministic and stochastic approaches to crosshole seismic travel-time inversions

The Bayesian inversion method is a stochastic approach based on the Bayesian theory.With the development of sampling algorithms and computer technologies,the Bayesian inversion method has been widely used in geophysical inversion problems.In this study,we conduct inversion experiments using crosshole seismic travel-time data to examine the characteristics and performance of the stochastic Bayesian inversion based on the Markov chain Monte Carlo sampling scheme and the traditional deterministic inversion with Tikhonov regularization.Velocity structures with two different spatial variations are considered,one with a chessboard pattern and the other with an interface mimicking the Mohorovicicdiscontinuity(Moho).Inversions are carried out with different scenarios of model discretization and source–receiver configurations.Results show that the Bayesian method yields more robust single-model estimations than the deterministic method,with smaller model errors.In addition,the Bayesian method provides the posterior probabilistic distribution function of the model space,which can help us evaluate the quality of the inversion result.

基于MCMC的页岩气藏压裂缝网不确定性反演法

水平井体积压裂是实现页岩气经济开发的有效手段。水力压裂能够形成三维立体复杂裂缝网络,改善页岩气藏渗流环境,但由于页岩气藏基础参数的不确定性,尤其是复杂的孔渗条件、地层温压系统及非线性渗流机理给页岩气压裂缝网反演带来了极大的不确定性,导致准确评估页岩气藏压裂效果困难。本文首先构建了联合直线分析法和图版拟合法的缝网参数确定性反演方法,然后讨论了缝网反演所需基础参数的不确定性,以此提出一种基于马尔可夫-蒙特卡洛方法(MCMC)的页岩气藏压裂缝网不确定反演方法,通过随机生成基础参数样本集,利用确定性方法解释不同样本集下的缝网关键参数,给出其多组合性及概率分布。最后,结合我国西南地区某页岩气区块一口体积压裂水平井的生产动态,明确了压裂缝网四个关键参数的不确定性排序,并反演出了关键参数值及P10、P50、P90的概率结果,评价了由于基础参数的不确定性对压裂缝网参数解释结果的影响程度,对页岩气藏压裂效果评价及合理开发对策制定提供了较全面的实际指导。

基于MCMC的DOA估计及其在空域滤波中的应用

针对传统算法在进行波达方向角估计时需要进行全空间的谱峰搜索,且依赖初始化迭代和插值的缺陷,设计了一种贝叶斯估计方法。首先,构建基于均匀圆阵的频域信号处理模型,提出了基于马尔科夫链蒙特卡罗进行波达方向角估计的方法,实现了无需初始化和插值的测向算法,且不需要进行全空间的谱峰搜索即可直接估计结果,同时推导了该模型下进行波达方向角估计的克拉美罗下界。在以测向结果为先验信息,同时设计了一种新的空域滤波增益模型。利用马尔科夫链蒙特卡罗算法对信号子空间进行解耦合,然后计算滤波器增益系数,以此实现空域滤波。最后在无线电设备搭建的真实环境中进行实验,验证了基于马尔科夫链蒙特卡罗算法进行阵列波达方向角估计和空域滤波的有效性,并且与其他模型相比要明显优于后者。

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.

基于MCMC的叠前地震反演方法研究

马尔科夫链蒙特卡洛方法(MCMC)是一种启发式的全局寻优算法.它在贝叶斯框架下,利用已有资料进行约束,既可使最优解满足参数的统计特性,又通过融入的先验信息,提高解的精度;寻优过程可跳出局部最优,得到全局最优解.利用MCMC方法,可以得到大量来自于后验概率分布的样本,不仅可以得到每个未知参数的估计值,而且可以得到与之相关的各种不确定性信息.此外,由于算法并不是利用有单一最优解的目标函数,所以结果对初始值的依赖不强.通过对简单一维层状介质模型的处理,和实际资料的应用,说明利用基于Metropolis-Hastings算法的MCMC方法进行地震反演,通过对解空间的随机搜索能够得到较好的效果.

基于NEATM和WISE数据的小尺寸近地小行星物理特性研究

小行星是太阳系中广泛分布的金属或岩石天体,直径从米级跨越到几百公里.它们蕴含了太阳系早期的信息,同时也可能会与地球轨道相交且撞击地球,因此研究小行星的物理参数、物质成分和表面性质对于了解太阳系行星的形成演化和近地天体防御具有重要意义.以国际小行星中心(Minor Planet Center,MPC)获取直径D<160 m的小尺寸近地小行星共67颗作为研究对象,其中包含部分潜在威胁小行星(Potentially Hazardous Asteroids,PHA).基于NEATM(Near-Earth Asteroid Thermal Model),使用广域红外巡天望远镜(Wide-field Infrared Survey Explorer,WISE)的观测数据,利用反射光模型对太阳反射光进行了修正,使用动力学模型计算WISE观测历元的小行星轨道数据,计算了这67颗小尺寸近地小行星的直径和反照率.拟合过程采用马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法,与WISE的研究结果和MPC的数据进行了比较分析,给出了其分类特征.研究为小行星的观测和理论提供了有力的支持,可以更好地了解近地小行星的特征和演化.

改进的MCMC方法及其应用

概率反演中,马尔科夫链蒙特卡罗是一类重要的后验概率抽样方法,但由于该算法的搜索往往会陷入局部最优解,因而限制了其在具有非唯一解反问题中的应用。鉴于此,本文对基于Metropolis-Hastings算法的多链搜索的方法进行了改进,改进后的方法可以根据搜索结果实时调整链的个数,因而可以在搜索到尽可能多的解的同时节省了多链搜索的时间。最后将该算法应用于一个地下水污染源反问题的求解,计算结果表明改进后的算法对求解非唯一性反问题具有较好的效果。