We describe a new algorithm for the inversion of one-dimensional shear-wave velocity profiles from dispersion curves of the fundamental mode of Rayleigh surface waves. The novelties of our approach are that the layer velocities and thicknesses are set as unknowns, and an artificial neural network is proposed to solve the inverse problem. We suggest that training data should be calculated for a set of random synthetic velocity layered models, while layer thicknesses and velocities should be set to fixed intervals, with ranges estimated based on the systematic application of empirical relations between Rayleigh and S-wave velocities to the dispersion data. Our main challenge is a total overhaul of the artificial neural network, which includes selecting the optimal artificial neural network architecture and parameters by performing a large number of numerical experiments. Our synthetic results show that the accuracy of the proposed approach outperforms that of the Monte Carlo approach. We illustrate our proposed method with West Siberia data processing obtained from an area of approximately 800 ${\text{km}}^2$. From a user perspective, the main strength of our method is the computationally efficient processing of large amounts of dispersion data, which make it well suited for four-dimensional near-surface monitoring.

中文翻译：

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一种用于表面波频散曲线反演的人工神经网络方法

我们描述了一种新算法，用于从瑞利面波基模的频散曲线反演一维横波速度剖面。我们方法的新颖之处在于将层速度和厚度设置为未知数，并提出了人工神经网络来解决逆问题。我们建议应该为一组随机合成速度分层模型计算训练数据，而层厚度和速度应该设置为固定间隔，范围估计是基于瑞利和 S 波速度之间的经验关系对分散数据。我们的主要挑战是对人工神经网络进行全面检修，其中包括通过执行大量数值实验来选择最佳人工神经网络架构和参数。我们的综合结果表明，所提出的方法的准确性优于蒙特卡罗方法。我们用从大约 800 个地区获得的西西伯利亚数据处理来说明我们提出的方法${\text{公里}}^2$. 从用户的角度来看，我们方法的主要优势是对大量弥散数据的计算高效处理，这使其非常适合四维近地表监测。