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Inversion of Rayleigh wave dispersion curves with an artificial neural network

机译:用人工神经网络反演瑞利波色散曲线

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The inversion method of Rayleigh wave dispersion curves had been discussed in this paper. At first, the authors studied the influence of elastic parameters on Rayleigh dispersion curves, and also studied the inflexions'movement of vR-λR dispersion curves with parameters' change. And then,the inversion method of artificial neural network was put forward for dispersion curves'inversion, and practical application was used to test the validity of the inversion method. According to the studied results, velocities of transverse waves and thicknesses of media have higher influence on dispersion curves, but densities and velocities of compressional waves have little influence on Rayleigh wave dispersion curves. Because the number of inflexions do not corresponding with the number of interfaces when parameters are changed at times. There is not a theoretic gist to delaminate by inflexions in Rayleigh waves' inversion. The artificial network method can interpret the dispersion curves, and it can be used in practice for the future.
机译:本文讨论了瑞利波分散曲线的反转方法。起初,作者研究了弹性参数对瑞利色散曲线的影响,并研究了VR-λR色散曲线的inflexions vr-λr色散曲线的变化。然后,提出了人工神经网络的反转方法,提出了分散曲线的转化,并且使用实际应用来测试反转方法的有效性。根据研究结果,横波和介质厚度对色散曲线的影响较高,但压缩波的密度和速度对瑞利波分散曲线几乎没有影响。因为当有时改变参数时,Inflexions的数量与接口数量不相对应。通过瑞利波反转中的Inflexions没有理论主体划分。人工网络方法可以解释色散曲线,可以在实践中用于未来。

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