水文地质参数反演的Hooke-Jeeves粒子群混合算法

水文地质参数寻优结果的好坏会直接影响到地下水数值模拟的精度,而参数寻优结果很大程度上取决于寻优方法的选择。粒子群算法是一种基于群智能的随机全局寻优方法,算法的缺陷是后期搜索效率低劣。基于随机寻优算法的混合策略,引入有效的约束处理手段和粒子群算法惯性因子的动态非线性调整技术,有机融合粒子群算法与Hooke-Jeeves方法,提出一种适用于水文地质参数反演的HJPSO混合算法。应用研究表明,HJPSO混合算法在参数反演计算中求解精度高、收敛速度快、寻优性能强,是一种值得推广的水文地质参数识别方法。     

基于Lévy飞行的粒子群算法在大地电磁反演中的应用

粒子群优化算法在大地电磁测深反演中相较于一般的线性反演算法具有多种优点。然而标准粒子群算法在多维优化问题中存在早熟问题,为此,采用基于Lévy飞行随机游走策略的优化粒子群算法来处理局部最优解,增加寻优能力。通过对地电模型的反演对比表明,改进后的粒子群算法相较于标准粒子群算法适应度值下降速度更快、寻优能力更好。最后将该算法应用于已知钻孔旁实测数据,结果较好,表明该算法具有较好的实用性。     

波阻抗约束反演的几种方法

介绍三种理论与实际效果比较好的波阻抗反演方法,即宽带约束反演(BCI)、快速模拟退火算法(FSA)和正则化方法。BCI是一种线性迭代方法,FSA是一种具有全局寻优特点的随机搜索方法,正则化方法是通过对目标函数施加先验约束,来解决反演的病态问题———多解性和不稳定性。最后,使用正则化方法并结合快速模拟退火算法,进行了理论模型试算和实际资料处理,实用效果比较好。     

地下水污染源反演的Hooke Jeeves吸引扩散粒子群混合算法

根据污染物质量浓度监测数据进行地下水污染源反演是一类典型的地下水逆问题,该问题可转化为决策变量为污染源位置和强度的最优化问题进行求解。基于Hooke-Jeeves粒子群混合算法,引入吸引扩散粒子群(ARPSO)算法的粒子群发散算子,保证混合算法的种群多样性,并提出HJ-ARPSO混合算法,再结合地下水污染物迁移模型MT3DMS反演地下水污染源的位置和强度信息。在已知污染源位置和未知污染源位置两种情形下,分别利用HJ-ARPSO算法、HJ-PSO算法和GA算法进行地下水污染源反演。在两种情形下,HJ-ARPSO算法均具有较高的寻优成功率(分别对应为100%和90%);与之相比,未引入粒子群发散算子的HJ-PSO算法在未知污染源位置情形下其寻优成功率迅速降为60%;GA算法寻优效率则最低。算例结果表明,HJ-ARPSO算法是一种有效的地下水污染源反演优化算法。     

波阻抗反演中的全局寻优策略

针对常规基于模型的波阻抗反演方法严重依赖于初始模型的选择和易陷入局部最优等局限性，提出了一种新的全局寻优策略。该反演策略是在常规测井约束反演的框架中加入一定次数的遗传全局搜索机制。如果迭代过程一旦陷入局部最优解；就可以把已经形成的局部最优解作为寻优的新起点，在此基础上进行全局遗传寻优，搜索更优的反演结果。通过模型数值计算和实际资料处理，表明该反演策略具有跳出初始模型控制的优良性能和良好的实际反演效果。     

基于粒子群优化算法的感应测井反演

针对传统感应测井线性迭代反演受初始模型影响,易陷入局部最优解的特点,设计一种基于粒子群优化的非线性全局最优化反演方法。利用该方法对不同厚度储层模型进行反演研究,在无噪声情况下,反演结果和模型基本一致;在加入5%、10%和15%随机噪声后,反演仍取得良好效果。数值实验结果表明,该反演方法不依赖于初始模型,具有较好的全局寻优和抗噪声能力,能有效反演感应测井数据。     

电阻率层析成像的二维改进粒子群优化算法反演

粒子群优化算法(PSO)是通过模拟鸟群觅食过程中的社会行为而提出的一种基于群体智能的全局随机搜索算法,已有研究学者证明PSO算法是一种有效的地球物理反演方法,不依赖初始模型。此次在研究常规粒子群算法的基础上,针对常规粒子群优化算法易于陷于局部极值,后期收敛速度慢,反演精度不高等缺点,提出了一种改进的充分混沌振荡粒子群优化算法。针对粒子群算法的特点,改进速度更新公式,使粒子更快获取与当前全局最好位置的差异,增强粒子的学习能力,并用此算法在matlab2012b编程环境中对均匀半空间电阻率层析成像异常体理论模型进行了二维数值试验。结果表明,此种算法反演时不依赖初始模型,搜索空间增大,实现全局搜索,在准确性上优于标准PSO反演,成像质量优于Levenberg-Marquardt法反演。     

板状体磁异常数据反演的PSO算法

粒子群优化(PSO)算法是根据鸟群觅食过程中的迁徙和群集模型而提出的用于解决优化问题的算法,是一类随机全局优化技术,它通过粒子间的相互作用搜索复杂空间中的最优区域,其优势在于效率高,且又简单易实现。本文讨论了PSO算法用于板状体磁异常数据反演的方法,并与遗传算法(GA)进行了比较。理论和实测磁异常数据反演的结果表明,PSO算法具有更高的找寻最优解效率,是一种很有潜力的位场反演工具。     

粒子群算法在磁测资料井地联合反演中的应用

粒子群优化算法是一种新兴的高效并行优化算法,目前被广泛应用于各领域,但在地球物理反演中很少运用.磁测资料井地联合反演是结合井中与地面磁测数据的各自优势,进行地球物理反演的一种新方法.笔者以立方体为模型,通过理论模型试验和大冶危机矿山实例,将粒子群优化算法运用于磁测资料井地联合反演,取得了比较好的效果,不仅验证了粒子群算法在地球物理反演中的可行性,而且也论证了井地联合反演的优越性.     

基于粒子群算法与混合罚函数法的有限元优化反演模型及应用

岩土工程优化反分析是一个典型的复杂非线性函数优化问题,采用全局优化算法是解决这个问题的理想途径。针对常规反演方法应用于岩土工程参数反演时搜索效率低的缺点,结合粒子群算法和遗传算法的特点,充分考虑二者的互补性,提出一种效率较高的全局优化算法,以测点的实测值与计算值建立一种新的评价函数,将多目标优化问题转化为单目标优化问题,用混合罚函数法将约束问题变为无约束问题,构建了一种新的目标函数,将有限元程序ABAQUS作为一个模块嵌入到优化算法程序中,编制了有限元优化反演分析程序。并给出了应用实例验证了该法的有效性和实用性,是一种可行的参数反演方法,可应用于实际工程中复杂岩土介质初始应力场反演、渗流场以及位移反分析     

A nonlinear multiparameter prestack seismic inversion method based on hybrid optimization approach

Multiparameter prestack seismic inversion is one of the most powerful techniques in quantitatively estimating subsurface petrophysical properties. However, it remains a challenging problem due to the nonlinearity and ill-posedness of the inversion process. Traditional regularization approach can stabilize the solution but at the cost of smoothing valuable geological boundaries. In addition, compared with linearized optimization methods, global optimization techniques can obtain better results regardless of initial models, especially for multiparameter prestack inversion. However, when solving multiparameter prestack inversion problems, the application of standard global optimization algorithms maybe limited due to the issue of high computational cost (e.g., simulating annealing) or premature convergence (e.g., particle swarm optimization). In this paper, we propose a hybrid optimization-based multiparameter prestack inversion method. In this method, we introduce a prior constraint term featured by multiple regularization functions, intended to preserve layered boundaries of geological formations; in particular, to address the problem of premature convergence existing in standard particle swarm optimization algorithm, we propose a hybrid optimization strategy by hybridizing particle swarm optimization and very fast simulating annealing to solve the nonlinear optimization problem. We demonstrate the effectiveness of the proposed inversion method by conducting synthetic test and field data application, both of which show encouraging results.     

S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm

The refraction microtremor method has been increasingly used as an appealing tool for investigating near surface S-wave structure. However, inversion, as a main stage in processing refraction microtremor data, is challenging for most local search methods due to its high nonlinearity. With the development of data optimization approaches, fast and easier techniques can be employed for processing geophysical data. Recently, particle swarm optimization algorithm has been used in many fields of studies. Use of particle swarm optimization in geophysical inverse problems is a relatively recent development which offers many advantages in dealing with the nonlinearity inherent in such applications. In this study, the reliability and efficiency of particle swarm optimization algorithm in the inversion of refraction microtremor data were investigated. A new framework was also proposed for the inversion of refraction microtremor Rayleigh wave dispersion curves. First, particle swarm optimization code in MATLAB was developed; then, in order to evaluate the efficiency and stability of proposed algorithm, two noise-free and two noise-corrupted synthetic datasets were inverted. Finally, particle swarm optimization inversion algorithm in refraction microtremor data was applied for geotechnical assessment in a case study in the area in city of Tabriz in northwest of Iran. The S-wave structure in the study area successfully delineated. Then, for evaluation, the estimated Vs profile was compared with downhole data available around of the considered area. It could be concluded that particle swarm optimization inversion algorithm is a suitable technique for inverting microtremor waves.     

改进型粒子群算法及其在GPR全波形反演中的应用

探地雷达作为高精度的物探工作方法,其主要目的是反演解释地下结构的物性参数。笔者提出社会学习型粒子群优化反演方法,它以信号均方误差为目标函数,用时域有限差分方法作正演,并且针对反射波信号较弱、反演效果不佳的情况设计了对正演结果进行振幅补偿的方法,对反射波的振幅进行增益,以提高反演精度。通过与经典粒子群优化反演方法的结果对比,说明了该算法在准确度以及效率方面都有相当大的提高。经过分析多层介质仿真数据的一维反演结果,说明了该算法对多参数反演的有效性和良好的抗噪性。     

基于微粒群算法的大坝材料参数反分析研究

将微粒群算法应用于大坝参数反分析，同时分析了群体规模对算法的搜索效率和搜索质量的影响以及微粒群反分析算法的数值稳定性。对算例的分析结果表明，基于微粒群算法的大坝参数反分析方法简便易行，收敛精度高，且具有很好的抗噪音能力，是一种新的有效、可靠的参数反分析方法。     

基于岩石物理模型的页岩储层裂缝属性及各向异性参数反演

针对富有机质页岩储层复杂的矿物组分与微观孔缝结构,本文提出基于岩石物理模型和改进粒子群算法的页岩储层裂缝属性及各向异性参数反演方法。应用自相容等效介质理论与Chapman多尺度孔隙理论建立裂缝型页岩双孔隙系统岩石物理模型。开发基于岩石物理模型的反演流程,引入模拟退火优化粒子群算法解决多参数同时反演问题,反演算法能够避免陷入局部极值且收敛速度快。将本文方法应用于四川盆地龙马溪组页岩气储层,反演得到的孔隙纵横比、裂缝密度等物性参数和各向异性参数与已有研究结果一致,能为页岩储层的评价提供多元化信息。     

位移反分析的粒子群优化-高斯过程协同优化方法

针对采用随机全局优化技术进行岩土工程位移反分析存在数值计算量大、效率低的问题,将粒子群优化算法与高斯过程机器学习技术相结合,提出了位移反分析的粒子群优化-高斯过程协同优化方法。该方法利用全局寻优性能优异的粒子群优化算法进行寻优的基础上,采用高斯过程机器学习模型不断地总结历史经验,预测包含全局最优解的最有前景区域,通过提高粒子群搜索效率并降低适应度评价次数,进而有效地降低位移反分析过程中的数值计算工作量。多种测试函数的数学验证和工程算例的研究结果表明该方法是可行的,与传统方法相比较,可显著地降低位移反分析的计算耗时。     

Slope stability analyzing using recent swarm intelligence techniques

Slope stability analysis of soil with a weak layer sandwiched between two strong layers is considered as a complex geotechnical problem. In this problem, the objective function is non‐convex and discontinuous with the presence of multiple strong local minima. Classical optimization techniques fail to converge to a valid solution unless a proper initial trial is adopted. Even though many new optimization algorithms have emerged, they have not been applied to geotechnical problems yet. In the present study, some recent swarm intelligence algorithms are adopted for some complicated example of slope stability problems and benchmarked with the traditional particle swarm optimization algorithm. From the results, it seems the levy flight krill herd algorithm is the most efficient method over proposed algorithms for this kind of problem. Copyright © 2014 John Wiley & Sons, Ltd.     

A comparative performance of least-square method and very fast simulated annealing global optimization method for interpretation of self-potential anomaly over 2-D inclined sheet type structure

Inversion of self-potential anomaly for 2-D inclined sheets of infinite horizontal extent has been studied. Least-square inversion and very fast simulated annealing global optimization has been used to model the five parameters of self potential anomaly. The method of least square and very fast simulated annealing global optimization method is compared and analyzed. Very fast simulated annealing can model the noisy and field data of self potential anomaly very precisely than linear inversion technique. However, time taken by very fast simulated annealing inversion is larger than linearized inversion. The comparative analysis has been done on synthetic data (noise free and noisy) and two field data from Bavarian woods anomaly, Germany and Surda anomaly, India to show the efficacy of both the methods. The estimated parameters were compared with those from previous studies using various global optimization algorithms, mainly neural network, genetic algorithm and particle swarm optimization on the same field data sets. It can be concluded that the global optimization algorithms considered in this study were able to yield compatible solutions with those from least-square methods. The present global optimization method is in good agreement with the other global optimization methods in terms of results and computation time.     

基于粒子群优化的岩土工程反分析研究

岩土工程优化反分析本质上看是一个典型的复杂非线性函数优化问题,采用全局优化算法是解决这个问题的理想途径,但由于优化反分析中多次调用正分析的特点使得整个算法的计算效率很低。为了提高优化反分析的计算效率,把一种计算效率更高的新型仿生算法--粒子群优化引入岩土工程反分析领域,提高反分析的计算效率。在此基础上,结合有限元数值分析技术,提出了一种新的岩土工程优化反分析算法--粒子群优化反分析。并通过一个简单算例验证了该法的有效性。