共查询到18条相似文献,搜索用时 171 毫秒
1.
2.
本文将量子蒙特卡罗全局优化方法引入地球物理反问题,进而发展了一类新的地球物理非线性反演方法.量子蒙特卡罗方法是基于量子力学机制的随机方法,包括变分蒙特卡罗方法、格林函数蒙特卡罗方法、扩散蒙特卡罗方法、路径积分蒙特卡罗方法.本文简要回顾了量子蒙特卡罗方法的发展,阐述了其方法理论;随后的数值试验结果表明,量子蒙特卡罗方法应用于地球物理反问题的求解是成功的,它适合于非线性、多极值的地球物理反演问题,在收敛速度和避免陷入局部极小等方面有着一定的优势,且该方法也适用于其他领域非线性最优化问题的求解,其算法具有较强的通用性;最后就量子蒙特卡罗方法在地球物理反问题中的应用前景以及存在的问题做了简要概述. 相似文献
3.
4.
5.
本文简要概述了量子计算的发展历程,阐述了量子计算的基本概念和特点,对常用的量子优化算法做了介绍.探讨了量子计算在计算地球物理领域的应用实例,并对量子计算在石油物探领域的应用前景做了分析,量子计算与人工智能的结合必将在油气勘探领域发挥重要作用. 相似文献
6.
波场梯度法是一种基于密集台阵记录的地震波形的地震数据处理方法,适用于多种地震信号/震相,比如P波、S波、Rayleigh波、Love波和环境噪声等.由于充分考虑了波场的时空变化,可以获得更多的地震波传播参数或介质参数,比如应力、旋度、地震波速度、方位角、几何扩散、辐射模式、方位各向异性和Q值等.自2007年波场梯度法的原理提出以来,该方法在河湾河谷的强地面运动研究、断层探测、月壳浅层结构成像、地球浅地表或地壳地幔速度结构和方位各向异性反演等方面得到较好的应用.基于不同的信号处理方法,波场梯度法也发展出不同的研究分支,比如基于傅里叶变换、小波变换或希尔伯特变换的波场梯度研究;基于不同参考坐标系、不同台网类型或不同震相/信号源也可以将波场梯度法划分为不同的研究方向.本文主要从方法原理、研究进展和方法比较对波场梯度法进行详细地描述,同时对其发展趋势进行简单地讨论. 相似文献
7.
8.
对地球物理中的非线性反演问题进行了讨论,地球物理反演通常涉及有限参数空间的最优问题,一个地球模型由一组参数描述其一个或多个地球物理性质(例如,穿过地球内部的弹性波速度)。地球模型是在一定的限制条件下,寻找模型预测值和观测值之间的最小失配。最优问题通常是非线性或非线性极强的反演问题,经常导致失配空间出现多重极小,在过去的10年里,全局(随机)最优方法得到了广泛应用,有关模拟退火、遗传算法和进化程序法的讨论已出现在有关的地球物理专业文献中。但是,这些方法在对各参数解的约束评价方面没有引起足够的重视,通常很少涉及这类问题。这里给出一类新的方法,该方法在反演的最优化和误差分析方面均具有潜力。新的方法使用的是计算几何的概念。这里描述的搜索方法对10维以上的问题不太适用。 相似文献
9.
地球物理信号是地下介质对物理场的响应,其特征是解释地下结构和性质的主要依据.但受限于地下介质构造及物性分布特征的复杂性,地球物理信号特征的识别和解释具有不确定性.机器学习基于数据与特征的映射关系为判别地球物理信号特征和解释提供了新的思路和方法.本文围绕机器学习方法在地球物理信号特征识别及解释应用主题,梳理得到机器学习用于地球物理信号特征识别与解释的一般逻辑思路和工作流程,在提炼机器学习所涉及的处理技术和评价体系的基础上,进一步总结了机器学习在解决岩石图像识别与分类、地层岩性预测与成图、地震事件检测和到时提取、微小地震信号解释等问题时的技术要点;并对深度学习模型和简单的机器学习模型针对不同地球物理信号进行特征识别与解释的适用性和应用实效进行了分析.针对目前的发展趋势和已有研究,对机器学习在地球物理信号特征识别应用方面进行了讨论和展望. 相似文献
10.
11.
12.
13.
14.
Adaptive hybrid global inversion algorithm 总被引:2,自引:0,他引:2
Most geophysical inversions can be regarded as multiparameter, nonlinear, and multiminimum discontinuous optimization problems.
An adaptive hybrid global inversion algorithm based on simulated annealing, downhill simplex method, uniform design, and adaptive
annealing rule is formulated. Numeral test and model computation show that this algorithm has very fast speed and high efficiency
in searching for global minimum.
Project sponsored by the National Natural Science Foundation of China (Grant No. 49474232) and Special Foundation under the
auspices of president of Chinese Academy of Sciences. 相似文献
15.
在地球物理非线性反演方法中,模拟退火法是一种较先进的启发式蒙特卡洛(MonteCarlo)方法.但是,在处理实际资料时,该方法存在着计算效率不够高的缺点,有时还会失效.为此,从模拟退少法的关键问题──最低温度的选择入手,根据模拟退火法与统计力学的吉布斯-马尔柯夫(Gibbs-Markov)模型之间的关系导出临界温度的近似表达式;用此式分析目标函数超曲面形状对模拟退火法计算的影响;提出利用模糊先验信息确定最低温度、改造目标函数等改进措施. 相似文献
16.
建议一种SA和GA相结合的策略,较好地解决了GA收敛早熟及SA搜索效率较低的问题,提高了全局优化计算效率;在应用其依据面波频散曲线反演工程场地剪切波速时,利用简化剥层法提供较小的模型空间,取得了较好的反演效果. 相似文献
17.
Linearized residual statics estimation will often fail when large static corrections are needed. Cycle skipping may easily occur and the consequence may be that the solution is trapped in a local maximum of the stack-power function. In order to find the global solution, Monte Carlo optimization in terms of simulated annealing has been applied in the stack-power maximization technique. However, a major problem when using simulated annealing is to determine a critical parameter known as the temperature. An efficient solution to this difficulty was provided by Nulton and Salamon (1988) and Andresen et al. (1988), who used statistical information about the problem, acquired during the optimization itself, to compute near optimal annealing schedules. Although theoretically solved, the problem of finding the Nulton–Salamon temperature schedule often referred to as the schedule at constant thermodynamic speed, may itself be computationally heavy. Many extra iterations are needed to establish the schedule. For an important geophysical inverse problem, the residual statics problem of reflection seismology, we suggest a strategy to avoid the many extra iterations. Based on an analysis of a few residual statics problems we compute approximations to Nulton–Salamon schedules for almost arbitrary residual statics problems. The performance of the approximated schedules is evaluated on synthetic and real data. 相似文献
18.
Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full‐waveform inversion,and residual static computation
下载免费PDF全文
![点击此处可从《Geophysical Prospecting》网站下载免费的PDF全文](/ch/ext_images/free.gif)
Angelo Sajeva Mattia Aleardi Bruno Galuzzi Eusebio Stucchi Emmanuel Spadavecchia Alfredo Mazzotti 《Geophysical Prospecting》2017,65(Z1):322-346
We compare the performances of four stochastic optimisation methods using four analytic objective functions and two highly non‐linear geophysical optimisation problems: one‐dimensional elastic full‐waveform inversion and residual static computation. The four methods we consider, namely, adaptive simulated annealing, genetic algorithm, neighbourhood algorithm, and particle swarm optimisation, are frequently employed for solving geophysical inverse problems. Because geophysical optimisations typically involve many unknown model parameters, we are particularly interested in comparing the performances of these stochastic methods as the number of unknown parameters increases. The four analytic functions we choose simulate common types of objective functions encountered in solving geophysical optimisations: a convex function, two multi‐minima functions that differ in the distribution of minima, and a nearly flat function. Similar to the analytic tests, the two seismic optimisation problems we analyse are characterised by very different objective functions. The first problem is a one‐dimensional elastic full‐waveform inversion, which is strongly ill‐conditioned and exhibits a nearly flat objective function, with a valley of minima extended along the density direction. The second problem is the residual static computation, which is characterised by a multi‐minima objective function produced by the so‐called cycle‐skipping phenomenon. According to the tests on the analytic functions and on the seismic data, genetic algorithm generally displays the best scaling with the number of parameters. It encounters problems only in the case of irregular distribution of minima, that is, when the global minimum is at the border of the search space and a number of important local minima are distant from the global minimum. The adaptive simulated annealing method is often the best‐performing method for low‐dimensional model spaces, but its performance worsens as the number of unknowns increases. The particle swarm optimisation is effective in finding the global minimum in the case of low‐dimensional model spaces with few local minima or in the case of a narrow flat valley. Finally, the neighbourhood algorithm method is competitive with the other methods only for low‐dimensional model spaces; its performance sensibly worsens in the case of multi‐minima objective functions. 相似文献