共查询到20条相似文献，搜索用时 296 毫秒
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快速模拟退火地震反演 总被引：10，自引：3，他引：10
讨论了用模拟退火方法进行地震资料的参数反演，利用快速的降温方式实现模拟退火反演，从而形成了快速模拟退火算法。模拟退火反演的优点是可以突破反演过程中局部最优的限制，获得全局最优解。因此，ＳＡ方法适于解决地震反演中的非凸性目标函数的最优化问题。 相似文献
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混杂的全局最优化算法是一种模拟退火法和下坡的单纯形法相结合的方法，该法用来反演远震体波求解震源参数，在时间域对震源时间函数加约束，反演双力偶参数而不是反演矩张互通主考虑多重震源导致非线性和多重模型问题，在这些问题中，目标函数包括许多局部极小值。 相似文献
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复杂介质波动方程反演是地球物理研究中的重要问题，通常表述为特定目标函数最优化，难点是多参数、非线性和不适定性.局部和全局优化方法都不能实现快速全局优化.本文概述了地震波勘探反演问题的理论基础和研究进展，阐述了反演中优化问题的解决方法和面临的困难，并提出了一种确定性全局优化的新方法.通过在优化参数空间识别并划分局部优化解及其附近区域，只需有限次参数空间划分过程就能发现所有局部解（集合）；基于复杂目标函数多尺度结构分析，提出多尺度参数空间分区优化方法的研究方向.该方法收敛速度快，优化结果不依赖初始解的选取，是对非线性全局优化问题的一个新探索. 相似文献
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层状介质参数反演的混合最优化法 总被引：8，自引：4，他引：4
波动方程反演是典型的非线性反问题,本文提出了用混合最优化法反演层状介质参数。混合最优化法将广义模拟退火与局部最优化方法结合,能较好地利用两者的优点,在本文中,局部最优化算法采用线性迭代算法。通过数值结果表明反演方法的正确有效性。 相似文献
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为解决地球物理反演中多解性的问题,综合多种地球物理信息的联合反演受到了广泛的关注.本文依据不同地球物理响应可能由相同异常体引起,而不同地球物理分布参数之间存在相关性等特点,提出了一种基于局部Pearson相关系数约束的联合反演方法.该方法假设每个局部区域模型参数的分布具有线性相关特性,在拟合不同类型观测数据时,对局部模型参数施加相关性约束,进行联合反演以减少多解性.本文采用交替迭代联合反演流程,改善了同一目标函数下联合反演收敛性和速度问题.基于新的联合反演方法和流程,我们测试了三维大地电磁和重力仿真数据的联合反演.结果表明,本文提出的基于局部相关性约束的联合反演方法,能充分利用大地电磁和重力观测数据信息,有效改善单一地球物理反演收敛性和多解性的问题,反演效果得到明显提升. 相似文献
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全波形反演方法是一个有效求解参数重建问题的方法,其本质是一个寻找最优解的优化问题,目前多用局部最优方法求解,如最速下降法、共轭梯度法、高斯-牛顿法、拟牛顿法等.文中给出了常用的优化方法,基于二雏声波方程,将这些方法应用于部分overthrust模型的反演,通过对各方法所得反演模型的精度和计算时间的对比分析,对各个方法的优缺点进行总结,为后续多参数反演或高维方程参数反演提供方法选择上的参考;针对所要求解的反问题,选用的优化方法需要在收敛速率、计算存储量和算法的稳定性之间进行权衡,以得到一个最优的反演结果. 相似文献
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叠前地质统计学反演将随机模拟与叠前反演相结合,不仅可以反演各种储层弹性参数,还提高了反演结果的分辨率.基于联合概率分布的直接序贯协模拟方法可以在原始数据域对数据进行模拟,不需要对数据进行高斯变换,拓展了地质统计学反演的应用范围;而联合概率分布的应用确保了反演参数之间相关性,提高了反演的精度.本文将基于联合概率分布的直接序贯协模拟方法与蒙特卡洛抽样算法相结合,参考全局随机反演策略,提出了基于蒙特卡洛优化算法的全局迭代地质统计学反演方法.为了提高反演的稳定性,我们修改了局部相关系数的计算公式,提出了一种新的基于目标函数的优化局部相关系数计算公式并应用到协模拟之中.模型测试及实际数据应用表明,该方法可以很好的应用于叠前反演之中. 相似文献
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Pantelis Soupios Irfan Akca Petros Mpogiatzis Ahmet T. Basokur Constantinos Papazachos 《Journal of Applied Geophysics》2011,75(3):479-489
Almost all earth sciences inverse problems are nonlinear and involve a large number of unknown parameters, making the application of analytical inversion methods quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem equations, adopting an iterative procedure which typically employs partial derivatives in order to optimize the starting (initial) model by minimizing a misfit (penalty) function. Unfortunately, especially for highly non-linear cases, the final model strongly depends on the initial model, hence it is prone to solution-entrapment in local minima of the misfit function, while the derivative calculation is often computationally inefficient and creates instabilities when numerical approximations are used. An alternative is to employ global techniques which do not rely on partial derivatives, are independent of the misfit form and are computationally robust. Such methods employ pseudo-randomly generated models (sampling an appropriately selected section of the model space) which are assessed in terms of their data-fit. A typical example is the class of methods known as genetic algorithms (GA), which achieves the aforementioned approximation through model representation and manipulations, and has attracted the attention of the earth sciences community during the last decade, with several applications already presented for several geophysical problems.In this paper, we examine the efficiency of the combination of the typical regularized least-squares and genetic methods for a typical seismic tomography problem. The proposed approach combines a local (LOM) and a global (GOM) optimization method, in an attempt to overcome the limitations of each individual approach, such as local minima and slow convergence, respectively. The potential of both optimization methods is tested and compared, both independently and jointly, using the several test models and synthetic refraction travel-time date sets that employ the same experimental geometry, wavelength and geometrical characteristics of the model anomalies. Moreover, real data from a crosswell tomographic project for the subsurface mapping of an ancient wall foundation are used for testing the efficiency of the proposed algorithm. The results show that the combined use of both methods can exploit the benefits of each approach, leading to improved final models and producing realistic velocity models, without significantly increasing the required computation time. 相似文献
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采用新近研制的分区多步不规则最短路径多震相地震射线追踪正演技术,结合流行的子空间反演算法,提出了一种联合多震相走时资料进行地震三参数 （速度、反射界面和震源位置） 同时反演的方法技术.数值模拟反演实例、以及与双参数 （速度和反射界面或速度和震源位置） 同时反演的对比分析表明：三参数同时反演成像结果大体接近双参数同时反演成像的结果.另外,噪声敏感性试验表明：所提算法对到时数据中可容许的随机误差并不敏感,结果说明多震相走时的联合三参数同时反演成像方法技术不失为一种提高走时成像空间分辨率、进而降低重建模型参数失真度、行之有效的方法技术. 相似文献
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Keurfon Luu Mark Noble Alexandrine Gesret Philippe Thierry 《Geophysical Prospecting》2020,68(2):536-552
The main goal of this study is to assess the potential of evolutionary algorithms to solve highly non-linear and multi-modal tomography problems (such as first arrival traveltime tomography) and their abilities to estimate reliable uncertainties. Classical tomography methods apply derivative-based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative-based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population-based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the differential evolution, the competitive particle swarm optimization and the covariance matrix adaptation–evolution strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty. By performing scalability and statistical analysis over the results obtained with several runs, we assess the benefits and shortcomings of each algorithm. 相似文献
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球坐标系下多震相走时三参数(速度、震源位置和反射界面)同时反演需要解决两个关键问题:(1)球坐标系下3D速度模型中多次透射、反射(折射)及转换波精确、快速的射线追踪;(2)同时反演时三种不同参数间的强耦合问题.为此,我们将直角坐标系下分区多步不规则最短路径算法推广至球坐标系中,进行区域或者全球尺度的多震相射线追踪.然后将其与适合多参数同时反演的子空间算法相结合,形成一种球坐标系下联合多震相走时三参数同时反演的方法技术.与双参数(速度和反射界面或速度和震源位置)同时反演的数值模拟对比分析显示:三参数与双参数的同时反演结果大体接近,并且它们对到时数据中可容许的随机噪声不太敏感.结果说明本文中的同时反演成像为一种提高成像分辨率,同时反演速度、震源位置和反射界面的有效方法. 相似文献
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The inversion of 3-D crustal structure and hypocenter location in the Beijing-Tianjin-Tangshan-Zhangjiakou area by genetic algorithm 总被引：1，自引：0，他引：1
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The classical genetic algorithm is a stochastic process which operates by natural selection. Although the algorithm may localize a point around the global minimum of the misfit function, it is not efficient at finding the precise solution. This paper suggests some hybrid genetic algorithms, derived from evolution theories, to overcome this problem. Firstly, sexual selection has been incorporated in the classical genetic algorithm to obtain a full representation of the Darwinist evolution concept. The simulation of sexual selection is performed by assigning a higher probability of surviving to some parameters that satisfy some algebraic relationships. This method is called the ‘marked constraints’ algorithm since it permits us to insert geological and geophysical constraints into the problem. The algorithm implementation is realized by progressively shrinking the parameter search space through successive generations. In this way, the genetic algorithm gains some degree of determinism. Secondly, since the evolution theory of Lamarck postulates that the acquired traits are passed on to the next generation, a hybrid use of the damped least‐squares method and the genetic algorithm is called Lamarckian inversion. Lamarckian inversion involves some improvement procedures that simulate the reduction of the misfit with the help of a derivative‐based method between two generations. Finally, although there is no correspondence in nature, Lamarckian and Darwinist evolution concepts are combined to strengthen the deterministic part of the solution algorithm. This is called the Lamarckian‐marked‐constraint algorithm. The merits and behaviours of the suggested algorithms are discussed using two examples. The first is a hypothetical example affected by a multiminima problem. The second examines the equivalence problem using vertical electrical sounding data. 相似文献
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本文针对层状介质并结合梯度法波形反演，提出逐层波形反演的方法. 首先给出介质扰动响应的概念，并在此基础上分析了梯度法波形反演方法. 波形反演实质上是将实测地震记录和预测地震记录的波形残差信息转化为实际地质模型和预测地质模型的模型残差信息. 波形反演的优点是利用大量振幅相位信息得到高分辨率的反演结果， 其缺点是运行耗时大；当初始模型和实际模型相差较大时，迭代算法容易陷入局部极小点，这是因为目标函数和初始模型同实际模型间的差异是非线性的关系. 逐层波形反演方法是使自上而下每一层的目标函数最小，这样总的目标函数也是最小的. 利用二分法速度扫描确定每一层速度不仅提高了运算速度也避免了迭代算法陷入局部极小点的问题. 结合介质扰动响应和目标函数值变化可以更为准确迅速地确定每一层速度和该层界面位置. 相似文献
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用随机启发式优化算法反演2003年云南大姚地震断层面参数的比较研究 总被引：1，自引：0，他引：1
基于余震分布确定主震断层面的数学模型,以确定断层面的走向和倾角参数进行计算,研究了遗传算法、模拟退火算法、差分演化算法、粒子群算法等4种最优化反演方法的反演效果和可靠性。结果显示,在涉及到的反演参数较少和非线性不太严重时,4种方法都有较好的表现,差分演化算法、粒子群算法速度快,精度高,遗传算法速度较慢,精度较低,模拟退火由于缺乏并行机制,速度较慢,精度高于遗传算法。余震在求出的断层附近分布图直观地反映出4种方法的效果和可靠性。 相似文献
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遗传算法在确定震源位置中的应用 总被引：8，自引：1，他引：8
地震定位需要震源的空间坐标和发震时刻，可以通过使观测走时和计算走时的拟合差达到最小来实现。遗传算法是解决此最优化问题的有效方法。本文对遗传算法确定震源位置的基本思路思路进行了说明和分析，讨论了拟合差随迭代次数的分布特征，最后，给出了最优化过程搜索震源位置在四维空间的分布。 相似文献
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