Implementation of Machine Learning Algorithms in Spectral Analysis of Surface Waves (SASW) Inversion

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.

Automated summary

The inversion procedure in spectral analysis of surface waves (SASW) data analysis is a complex process that requires an initial assumption of soil profile and calculating theoretical dispersion curves. Automating the inversion process allows for convenient and rapid evaluation of soil stiffness properties. Support vector regression (SVR) algorithms show potential in estimating shear wave velocity of soil based on dispersion curves obtained from field tests.