###### Abstract
To overcome the difficulties associated with the presence of multiple modes in SASW signals, [PERSON] and [PERSON] [17] introduced a new inversion method based on the maximum vertical flexibility. [PERSON] and [PERSON] [18] proposed the back-calculation neural network to replace the intensive computation of trial and error and the least-squares inversion method. Studies related to SASW inversion are summarized in Table 1.  
The complexity of the SASW method in terms of data reduction has encouraged researchers to find a much more straightforward alternative without rel  
Figure 1: Variation of Rayleigh wave particle motion with depth for a half-space with different values of Poisson’s ratio, \(v\)[8]. Another motivation for much simpler analysis is to make SASW available for all users, be they experienced or beginners. Recently, a guideline for nonexpert users in surface wave acquisition and analysis has been published by [PERSON] et al. [19]. The guideline presented a wide range of concerns related to the surface wave technique, including the nonuniqueness problem of SASW solutions. In conventional SASW inversion analysis, a soil profile needs to be assumed for the inversion process, which involves calculating the theoretical dispersion curve by means of forward modeling [24]. A misfit between the experimental curve and the theoretical dispersion curve is then iteratively and automatically minimized to a predefined small value. If the assumption of the starting soil profile model is not reasonably close, the iteration process might lead to nonconvergence or take too long to be converged. Therefore, each step in the inversion process requires careful execution. Expert user opinions are often necessary in order to dispose of uncertain interpretation of the shear wave velocity profile and define the criteria for a final profile. To improve the complexity of the inversion process, some machine learning (ML) algorithms were proposed in this study. The main objective of the present study is to accelerate and simplify the inversion process by training the selected ML algorithms to 'understand' how conventional SASW inversion comes out with a shear wave velocity profile from the corresponding experimental dispersion curve, as illustrated in Figure 2.  
An artificial neural network (ANN) is a popular ML model which has been adopted in recent years to automate the SASW inversion method [23; 25; 26; 27]. [PERSON] and [PERSON] [23] determined the moduli and thicknesses of a four-layered pavement system through the use of an ANN. Later, [PERSON] et al. [27] improved the models to develop five ANN models, with each model determining one single property of the pavement system. [PERSON] et al. [25] developed a number of different ANN models to automate the SASW method in pavements. They used several points of the dispersion curve as input and the thicknesses and the elastic moduli of the layers as output. Three ANN software packages (STATISTICA, ANN toolbox by MATLAB, and NeuralSIM) were employed and the models could generate reasonably close elastic modulus for the upper layers. [PERSON] et al. [26] used the results of the SASW tests to train ANN for the classification of shear wave velocity. They conducted nine SASW