Neural Network Application in Dispersion Curve Inversion of Seabed Geoacoustic Parameters

Abstract

This paper estimates seabed shear-wave velocities and the thickness of the surface sediments using Scholte wave dispersion curves extracted from data. Common surface wave dispersion curve inversion methods are divided into: local linearization methods and global optimization methods. These are model-driven, the inversion process takes a lot of time, and it is easy to get a local optimum, and the inversion results are inaccurate. Aiming at the shortcomings of the existing surface wave dispersion inversion methods, this paper introduces an inversion method based on neural network, and fits the Scholte wave dispersion curve to obtain the geoacoustic parameters of the surface sediments. Neural network inversion is data-driven, and the model is extracted from the data, which can improve the speed and accuracy of surface wave inversion. By simulating the shallow sea model, better results are obtained, and at the same time, the experimental data is used for calculation, the inversion results are close to the traditional methods, and the inversion speed is improved.