In this work, by introducing the seismic impedance tensor we propose a new Rayleigh wave dispersion function in a homogeneous and layered medium of the Earth, which provides an efficient way to compute the dispersion curve -- a relation between the frequencies and the phase velocities. With this newly established forward model, based on the Mixture Density Networks (MDN) we develop a machine learning based inversion approach, named as FW-MDN, for the problem of estimating the S-wave velocity from the dispersion curves. The method FW-MDN deals with the non-uniqueness issue encountered in studies that invert dispersion curves for crust and upper mantle models and attains a satisfactory performance on the dataset with various noise structure. Numerical simulations are performed to show that the FW-MDN possesses the characteristics of easy calculation, efficient computation, and high precision for the model characterization.
翻译:在这项工作中,通过引入地震阻力拉强,我们提议在地球的同质和层介质介质中建立新的Rayleigh波分散功能,这为计算分散曲线提供了一种有效的方法 -- -- 频率和阶段速度之间的关系。有了这一新建立的远期模型,以混合密度网络(MDN)为基础,我们开发了一种机器学习的反向方法,称为FW-MDN,用于从分散曲线中估计S波速度的问题。FW-MDN方法处理在对地壳和上层层模型进行反向分散曲线的研究中遇到的非独特性问题,并取得不同噪音结构在数据集上令人满意的性能。进行了数值模拟,以显示FW-MDN具有简单计算、高效计算和高精确模型特征特征的特性。