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Direct and Evolutionary Approaches for Optimal Receiver Function Inversion.

机译：最优接收机功能求逆的直接和进化方法。

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摘要

Receiver functions are time series obtained by deconvolving vertical component seismograms from radial component seismograms. Receiver functions represent the impulse response of the earth structure beneath a seismic station. Generally, receiver functions consist of a number of seismic phases related to discontinuities in the crust and upper mantle. The relative arrival times of these phases are correlated with the locations of discontinuities as well as the media of seismic wave propagation. The Moho (Mohorovicic discontinuity) is a major interface or discontinuity that separates the crust and the mantle. In this research, automatic techniques to determine the depth of the Moho from the earth's surface (the crustal thickness H) and the ratio of crustal seismic P-wave velocity (Vp) to S-wave velocity (Vs) (kappa= Vp/Vs) were developed.;In this dissertation, an optimization problem of inverting receiver functions has been developed to determine crustal parameters and the three associated weights using evolutionary and direct optimization techniques. The first technique developed makes use of the evolutionary Genetic Algorithms (GA) optimization technique. The second technique developed combines the direct Generalized Pattern Search (GPS) and evolutionary Fitness Proportionate Niching (FPN) techniques by employing their strengths. In a previous study, Monte Carlo technique has been utilized for determining variable weights in the H-kappa stacking of receiver functions. Compared to that previously introduced variable weights approach, the current GA and GPS-FPN techniques have tremendous advantages of saving time and these new techniques are suitable for automatic and simultaneous determination of crustal parameters and appropriate weights.;The GA implementation provides optimal or near optimal weights necessary in stacking receiver functions as well as optimal H and kappa values simultaneously. Generally, the objective function of the H-kappa stacking problem displays multimodal surfaces with multiple local and global optima. Niching mechanism permits standard GAs to identify different subpopulations representing various peaks. In multimodal optimization, fitness sharing has been commonly used to generate stable subpopulations of individuals around multiple optimum points in the search space. In this study the newly developed FPN is implemented to identify the different local and global optima regions (niches).;"Survival of the fittest" from evolutionary concepts is the basis for GA and the approximate location of the highest fitness individual (global optima) is quickly identifiable from the FPN niche masters (cluster centers). Using the approximate global optima location from the FPN as an initial point, the GPS technique provides quicker and optimal solutions for the five variables under investigation---the crustal thickness, Vp/Vs ratio and the three associated weights.;Applications of GA and GPS-FPN using seismic data from seismic stations within Ethiopia and surrounding the East Africa Rift System provided results which are consistent with previously published studies. The GPS technique is among the very few provably convergent, derivative-free search methods for linearly constrained optimization problems. GPS is shown in this study to be a powerful optimization tool that provides consistent results as if it searches the parameter space exhaustively. However, GPS searches the parameter space only in a given pattern and computes objective function values at few points. Key features of GPS technique reported in this study also include repeatability of its results, unlike heuristic search approaches, repeatability of the number of iterations as well as the number of objective function evaluations as long as initial values, the lower and upper bounds, and the processing machine stay the same. GPS even produces consistently similar results irrespective of initial values. The limitation of GPS being sometimes trapped at a local optimum is solved in this study by combining it with FPN.
机译：接收机函数是通过将垂直分量地震图与径向分量地震图解卷积而获得的时间序列。接收器功能代表地震台下地球结构的脉冲响应。通常，接收器功能由与地壳和上地幔不连续有关的许多地震阶段组成。这些相的相对到达时间与不连续的位置以及地震波传播的介质相关。 Moho（Mohorovicic不连续面）是将地壳和地幔分开的主要界面或不连续面。在这项研究中，自动技术用于确定距地球表面的莫霍面深度（地壳厚度H）以及地壳地震P波速度（Vp）与S波速度（Vs）的比率（kappa = Vp / Vs本文研究了一种利用演化和直接优化技术确定接收机参数和三个相关权重的反接收函数的优化问题。开发的第一项技术利用了进化遗传算法（GA）优化技术。开发的第二种技术结合了直接的通用模式搜索（GPS）和进化适应度适当小生境（FPN）技术的优势。在先前的研究中，蒙特卡洛技术已被用于确定接收器函数的H-kappa叠加中的可变权重。与以前引入的可变权重方法相比，当前的GA和GPS-FPN技术具有节省时间的巨大优势，这些新技术适用于自动和同时确定地壳参数和合适的权重。同时叠加接收器功能所需的权重以及最佳H和kappa值。通常，H-kappa堆积问题的目标函数显示具有多个局部和全局最优值的多峰曲面。利基机制允许标准GA识别代表各种峰的不同亚群。在多模式优化中，适应度共享通常用于在搜索空间中的多个最佳点附近生成个体的稳定子种群。在这项研究中，新开发的FPN用于识别不同的局部和全局最佳区域（小生境）。；进化概念的“适者生存”是遗传算法和最高适应个体的近似位置（全局最佳）的基础。可以快速从FPN利基市场（集群中心）中识别出来。 GPS技术以FPN的近似全球最佳位置为起点，为被调查的五个变量-地壳厚度，Vp / Vs比率和三个相关权重提供了更快，更优化的解决方案; GA和GPS-FPN使用来自埃塞俄比亚和东非裂谷系统周围地震台站的地震数据提供的结果与以前发表的研究一致。 GPS技术是针对线性约束优化问题的极少数可证明收敛，无导数搜索方法之一。 GPS在这项研究中显示是一种功能强大的优化工具，可以提供一致的结果，就好像它穷举搜索参数空间一样。但是，GPS仅以给定模式搜索参数空间，并在几个点上计算目标函数值。这项研究报告的GPS技术的关键特征还包括其结果的可重复性，这与启发式搜索方法不同，迭代次数的可重复性以及目标函数评估的次数（只要初始值，上下限以及处理机保持不变。无论初始值如何，GPS甚至都能产生一致的相似结果。这项研究将GPS与FPN结合起来，解决了GPS有时局限在局部最优状态下的局限性。

著录项

  • 作者

    Dugda, Mulugeta Tuji.;

  • 作者单位

    North Carolina Agricultural and Technical State University.;

  • 授予单位 North Carolina Agricultural and Technical State University.;
  • 学科 Engineering Electronics and Electrical.;Geophysics.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 148 p.
  • 总页数 148
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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