Surface wave methods have received much attention due to their efficient, flexible and convenient characteristics. However, there are still critical issues regarding a key step in surface wave inversion. In most existing methods, the number of layers is assumed to be known prior to the process of inversion. However, improper assignment of this parameter leads to erroneous inversion results. A Bayesian nonparametric method for Rayleigh wave inversion is proposed herein to address this problem. In this method, each model class represents a particular number of layers with unknown S-wave velocity and thickness of each layer. As a result, determination of the number of layers is equivalent to selection of the most applicable model class. Regarding each model class, the optimization search of S-wave velocity and thickness of each layer is implemented by using a genetic algorithm. Then, each model class is assessed in view of its efficiency under the Bayesian framework and the most efficient class is selected. Simulated and actual examples verify that the proposed Bayesian nonparametric approach is reliable and efficient for Rayleigh wave inversion, especially for its capability to determine the number of layers.

面波方法因其高效，灵活和方便的特性而备受关注。但是，关于表面波反演的关键步骤仍然存在关键问题。在大多数现有方法中，假定在反转过程之前已知层数。但是，此参数分配不当会导致错误的反演结果。本文提出了一种用于瑞利波反演的贝叶斯非参数方法来解决这个问题。在这种方法中，每个模型类代表具有未知S波速度和每一层厚度的特定数量的层。结果，确定层数等同于选择最适用的模型类别。关于每个模型类别，利用遗传算法实现了各层横波速度和厚度的优化搜索。然后，根据贝叶斯框架下的效率评估每个模型类别，并选择最有效的类别。仿真和实际算例验证了所提出的贝叶斯非参数方法对于瑞利波反演是可靠且有效的，尤其是其确定层数的能力。