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反演瑞雷波频散曲线能有效地获取横波速度和地层厚度,传统的多模式瑞雷波频散曲线反演需要正确的模式判别.然而,当地层中含有低速软弱夹层或高速硬夹层等复杂结构时,瑞雷波可能会出现“模式接吻”和“模式跳跃”等现象,这些现象极易造成模式误判,进而导致错误的反演结果;同时,传统的频散曲线反演方法需要进行求根运算,进而导致现有的瑞雷波非线性反演速度慢,运算时间长.鉴于此,对传统的Haskell-Thomson频散曲线正演模拟算法进行了改进,提出了一种新颖有效的目标函数.该目标函数直接利用实测频散曲线与迭代更新模型频散函数表面形状进行最佳拟合,无需将多模式频散数据归于特定的模式,可有效避免多模式瑞雷波频散曲线反演模式误识别;同时,该目标函数不需要求根运算,进而大大加快了非线性反演速度.基于粒子群优化算法,利用实际工作中经常遇到的3种典型理论地质模型和某一高速公路路基实测资料进行了理论模型试算和实例分析,检验了本文提出的瑞雷波多模式频散曲线反演新方法的有效性和实用性.
Inversion of Rayleigh wave dispersion curve can effectively obtain the shear wave velocity and formation thickness, and the traditional multi-mode Rayleigh wave dispersion curve inversion requires correct mode discrimination. However, when the formation contains low-speed weak or high-speed hard interlayer Complex structure, Rayleigh waves may appear “mode kiss ” and “mode jump ” and other phenomena, these phenomena can easily lead to misjudgment of the model, which led to false inversion results; the same time, the traditional dispersion curve The inversion method needs root-seeking, which leads to the non-linear inversion of existing Rayleigh waves is slow and the computation time is long. In view of this, the traditional Haskell-Thomson dispersion curve forward modeling algorithm is improved, A novel and effective objective function that directly fits the best fit of the surface shape of the dispersion function of the iteratively updated model using the measured dispersion curve without the need to assign the multimodal dispersion data to a particular pattern and effectively avoids the multi- In the meantime, the objective function does not need to find the root operation, which greatly accelerates the nonlinear inversion speed.Based on Particle Swarm Optimization (PSO) algorithm, using the actual work often To three typical geological model and theory of a highway embankment measured data and theoretical model test case analysis, examined the proposed multi-mode Rayleigh wave dispersion curve inversion scheme’s effectiveness and practicality.