DispersioNET: Joint Inversion of Rayleigh-Wave Multimode Phase Velocity Dispersion Curves using Convolutional Neural Networks

Autor:	Sharma, Rohan, Vashisth, Divakar, Shekar, Bharath
Rok vydání:	2023
Předmět:	Physics - Geophysics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse problem is ill-posed, non-unique and nonlinear. Here, we introduce DispersioNET, a deep learning model based on convolution neural networks (CNN) to perform the joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves. DispersioNET is trained and tested on both noise-free and noisy dispersion curve datasets and predicts S-wave velocity profiles that match closely with the true velocities. The architecture is agnostic to variations in S-wave velocity profiles such as increasing velocity with depth and intermediate low-velocity layers, while also ensuring that the output remains independent of the number of layers. Comment: Preprint of the paper submitted to the 2nd Indian Near Surface Geophysics Conference & Exhibition by AF Academy and the European Association of Geoscientists & Engineers (EAGE)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.14094 Zobrazit plný text záznamu View this record from Arxiv