###### Abstract  
One of the complex processes in spectral analysis of surface waves (SASW) data analysis is the inversion procedure. An initial soil profile needs to be assumed at the beginning of the inversion analysis, which involves calculating the theoretical dispersion curve. 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. Automating the inversion procedure will allow us to evaluate the soil stiffness properties conveniently and rapidly by means of the SASW method. Multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and linear regression (LR) algorithms were implemented in order to automate the inversion. For this purpose, the dispersion curves obtained from 50 field tests were used as input data for all of the algorithms. The results illustrated that SVR algorithms could potentially be used to estimate the shear wave velocity of soil. spectral analysis of surface wave; inversion; automation; machine learning +
Footnote †: journal: Applied Sciences  
_Applied_  
**C1**: _Academic Editor._ [PERSON] Received: 22 February 2021  
**Y. M.  
One of the complex processes in SASW data analysis is the inversion procedure. Studies related to the inversion procedure can go in many directions due to the procedure's complexness, which includes generation of the theoretical dispersion curve method, simplification of the procedure, problems with multi modes of the dispersion curve, and nonuniqueness of the solution. [PERSON] [9] used a modification of the Haskell-Thomson technique [10; 11] to generate the theoretical dispersion curve. He employed the INVERT program, which is computer software designed for assuming the initial stiffness profile reported by [PERSON] [12], to compare the shear wave velocities directly. The differences between the shear wave velocity profiles from [PERSON]'s results and those from the [PERSON] study were quite drastic. Therefore, the simplified inversion process based upon scaling the dispersion curve employed by many researchers is not suitable. [PERSON] and [PERSON] [13] found some limitations in [PERSON]'s technique. The method used to find the roots of the characteristics determinant for a layered system is quite complicated. Another limitation is the trial-and-error process of matching the theoretical and experimental dispersion curves. [PERSON] and [PERSON] applied two methods, using the FORTRAN 77 program to determine the pavement moduli and thicknesses to overcome these difficulties, while they later developed a finite difference technique to analyze the generalized Rayleigh waves in multilayered elastic media. This method is very significant when it comes to determining the theoretical dispersion curve for a pavement system, which leads to a quadratic eigenvalue problem for the pavement system and a linear eigenvalue problem for the geologic model. The second technique comprised using a modification of [PERSON]'s [14] algorithm to determine the theoretical dispersion curve. Later on, [PERSON] and [PERSON] [15] generated an algorithm based on the energy integral equation to overcome this problem. Moreover, [PERSON] and [PERSON] [16] developed a unique SASW system requiring no spectrum analyzer. Micro-computerization was used to conduct the entire procedure, and is able to determine the in situ dispersion curve. A program called \"SASWFM\" was written to automate the inversion process. The shear wave velocity can be obtained directly from the experimental dispersion curve. The results were compared to Seismic Cone Penetration Testing (SCPT) tests and reflected a relatively poor match between the experimental curve and the theoretical dispersion curve in some of the sites. Overall, the microcomputer-based system reduces the time for testing and the cost of equipment.