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Inversion of subsurface physical parameters using modem optimization methods

使用现代优化方法反演地下物理参数

基本信息

批准号：
15510147
负责人：
YAMANAKA Hiroaki
金额：
$ 2.11万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

项目摘要

Inversion of subsurface physical parameters from earthquake or microtremor data is one of the often used works in Earthquake Engineering. Usually least-square method is applied in the inversion. However, we have difficulties to find an optimal model because of nonlinear characteristics of misfit function to be minimized. Recently several modem optimization methods without use of gradient information of misfit surface are developed in the field of operation research. In this study, we investigated applicability of heuristic search methods in inversion of wave data.First, three heuristic search methods, Genetic Algorithm, Simulated Annealing and Tabu Search are implemented to invert Rayleigh wave phase velocity for shallow S-wave velocity profiling. Unlike linearized least-squares inversion, they do not require derivative calculation, or an initial mode, only the forward modeling calculation. The performances of the three heuristic techniques are compared with numerical experiments. With … More common paramerization and search limits, the three algorithms can reconstruct well shallow S-wave profiles with and without a velocity reversal from the synthetic data without any specific initial models. The Genetic Algorithms and the Simulated Annealing show a fast convergence of the misfits. However, the Simulated Annealing can find models with the smallest misfits. It is also found that the decrease of the misfits is the most gradual in the Tabu Search. Next we applied the three methods to inversion of spectral ratios of earthquake records from a vertical bore hole array to estimate S-wave velocity and Q-values. We can observe the same conclusions as obtained above. Furthermore the simulated annealing is used to a joint inversion of resistivity sounding data and microtrernor H/V spectral data. We can estimate resistivity and S-wave profiles simultaneously from this joint inversion. Finally, neural network technique is applied to automatic estimation of onset times of initial P-and S-waves. We can estimate travel time delays due to sedimentary layers in Tokyo area from the estimated travel time data. Less

由地震或地脉动资料反演地下物理参数是地震工程中经常使用的工作之一。反演中通常采用最小二乘法。然而，由于失配函数的非线性特性，我们很难找到一个最佳的模型。近年来，在运筹学领域中发展了几种不利用错配曲面梯度信息的现代优化方法。本文研究了启发式搜索方法在地震波数据反演中的适用性，首先，采用遗传算法、模拟退火算法和禁忌搜索三种启发式搜索方法对浅层横波速度剖面进行了瑞利波相速度反演。与线性化最小二乘反演不同，它们不需要导数计算或初始模式，仅需要正演模拟计算。三个启发式技术的性能进行了比较与数值实验。与 ...更多信息这三种算法具有共同的参数化和搜索范围，不需要任何特定的初始模型，就可以从合成数据中很好地重建有速度反演和无速度反演的浅层横波剖面。遗传算法和模拟退火算法对失配问题具有快速收敛性。然而，模拟退火可以找到具有最小失配的模型。研究还发现，在禁忌搜索中，失配的减少是最渐进的。然后，我们应用这三种方法反演了垂直钻孔阵列地震记录的频谱比，以估计S波速度和Q值。我们可以观察到与上述相同的结论。并将模拟退火算法应用于电阻率测深数据和微电阻率H/V谱数据的联合反演。通过这种联合反演可以同时估计电阻率和横波剖面。最后，将神经网络技术应用于初始P波和S波起始时刻的自动估计。我们可以估计旅行时间延迟由于沉积层在东京地区从估计的旅行时间数据。少