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

地下物理参数反转地震或微晶体数据是地震工程中经常使用的作品之一。通常在反转中应用最小二乘法。但是，我们很难找到一个最佳模型，因为Misdfit函数的非线性特征要最小化。最近，在操作研究领域开发了几种未使用MIDDFIT表面梯度信息的调制解调器优化方法。在这项研究中，我们研究了启发式搜索方法在波数据反转中的适用性。首先，实施了三种启发式搜索方法，遗传算法，模拟退火和禁忌搜索，以反转雷利波波相位速度的浅层S波速度分析。与线性化最小二乘反演不同，它们不需要衍生计算或初始模式，而仅需要正向建模计算。将三种启发式技术的性能与数值实验进行了比较。使用…更常见的参数化和搜索限制，这三种算法可以在没有任何特定初始模型的情况下从合成数据中逆转速度逆转和不带速度逆转。遗传算法和模拟退火显示了误贴的快速收敛。但是，模拟的退火可以找到最小的误差模型。还发现，不属性的下降是禁忌搜索中最高的成绩。接下来，我们将这三种方法应用于地震记录的光谱比率的反转，从垂直孔孔阵列到估计S波速度和Q值。我们可以观察到与上述相同的结论。此外，模拟退火用于抗性发声数据和微元素H/V光谱数据的联合反转。我们可以简单地从该关节反转来估算抗性和S波轮廓。最后，将神经网络技术应用于初始P和S波的发作时间的自动估计。我们可以根据估计的旅行时间数据在东京地区的沉积层估计旅行时间延迟。较少的