JointNet: A Multimodal Deep Learning-Based Approach for Joint Inversion of Rayleigh Wave Dispersion and Ellipticity

Abstract

Joint inversion of multitype datasets is an effective approach for high-precision subsurface imaging. We present a new deep learning-based method to jointly invert Rayleigh wave phase velocity and ellipticity into shear-wave velocity of the crust and uppermost mantle. A multimodal deep neural network (termed JointNet) is designed to analyze these two independent physical parameters and generate outputs, including velocity and layer thicknesses. JointNet is trained using random 1D models and corresponding synthetic phase velocity and ellipticity, resulting in a low cost for the training dataset. Evaluation using synthetic and observed data shows that JointNet produces highly comparable results compared to those from a Markov chain Monte Carlo-based method and significantly improves inversion speed. Training using synthetic data ensures its generalized application in various regions with different velocity structures. Moreover, JointNet can be easily extended to include additional datatypes and act as a joint inversion framework to further improve imaging resolution.