Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

Abstract

Surface wave inversion is a key step in the application of surface waves to soil velocity profiling. Currently, a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable. However, an improper selection of the number of layers may lead to an incorrect shear wave velocity profile. In this study, a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers. First, a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number. Then, the shear-wave velocity profile is determined by a genetic algorithm with the known layer number. By applying this procedure to both simulated and real-world cases, the results indicate that the proposed method is reliable and efficient for surface wave inversion.