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Application of the Coupled Local Minimizers Method to the Optimization Problem in the Spectral Analysis of Surface Waves Method

机译:耦合局部最小化方法在表面波谱分析中的优化问题中的应用

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The spectral analysis of surface waves (SASW) method aims to determine the small strain dynamic soil characteristics of shallow soil layers. The method involves an in situ experiment, the determination of an experimental dispersion curve, and the solution of an inverse problem, formulated as a nonlinear least squares problem. The latter is usually solved with a gradient-based local optimization method, which converges fast, but does not guarantee to find the global minimum of the objective function. The method of coupled local minimizers (CLM) combines the advantage of gradient-based local algorithms with the global approach of genetic algorithms. A cooperative search mechanism is set up by simultaneously performing a number of local optimization runs that are coupled by pairs of synchronization constraints. A synthetic example with two design variables (the shear wave velocity of two top layers of a layered half-space consisting of three layers on a half-space), demonstrates that the CLM method succeeds in finding the global minimum of an objective function with multiple minima and can successfully be used to solve the inverse problem in the SASW method. This is further illustrated by a complete inversion of the shear wave velocity profile accounting for seven design variables (the thickness and shear wave velocity of the three layers and the shear wave velocity of the underlying half-space). The inversion algorithm based on the CLM method is subsequently applied to invert the experimental dispersion curve derived from in situ data collected at a test site in Saluggia, Italy, consisting mainly of alluvial sediments. Up to a depth of about 25 m, the results show a reasonably good correspondence with crosshole test results.
机译:地表波谱分析(SASW)方法旨在确定浅土层的小应变动态土特征。该方法涉及原位实验,实验色散曲线的确定以及反问题的求解,这些问题被公式化为非线性最小二乘问题。后者通常使用基于梯度的局部优化方法来求解,该方法可以快速收敛,但不能保证找到目标函数的全局最小值。耦合局部最小化器(CLM)方法将基于梯度的局部算法的优点与遗传算法的全局方法相结合。通过同时执行许多本地优化运行(通过成对的同步约束耦合)来建立协作搜索机制。一个带有两个设计变量的合成示例(一个分层半空间的两个顶层的剪切波速度,该分层空间由一个半空间上的三个层组成),表明CLM方法成功地找到了具有多个目标函数的全局最小值最小且可以成功地用于解决SASW方法中的反问题。这可以通过考虑七个设计变量(三层的厚度和剪切波速度以及下面的半空间的剪切波速度)的剪切波速度曲线的完全反转来进一步说明。随后应用基于CLM方法的反演算法来反演从意大利Saluggia的一个测试地点收集的现场数据得出的实验色散曲线,该数据主要由冲积沉积物组成。在约25 m的深度处,结果显示出与井孔测试结果相当良好的对应关系。

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