Quality classification and inversion of receiver functions using convolutional neural network

Convolutional neural network (CNN) is presented to implement quick quality classification and inversion for teleseismic P-wave receiver functions (RF). For the first case, a CNN is trained using field measured RFs from NE margin of the Tibetan Plateau to efficiently predict the quality of each input waveform. Signal-to-noise ratio and correlation are introduced to quantitatively determine the quality label of RF, avoiding the subjectivity of manual labelling. The trained network reduces the time needed for data processing and has higher accuracy and efficiency than conventional methods. Its good performance is confirmed by comparing it with manually selected data from NE of the Tibetan Plateau. The second case is an example of joint inverting teleseismic P-wave RF and surface wave dispersions for the estimation of earth S-wave structure and associated uncertainties. We train a UNet based on synthetic global Crust 5.1 models and standard earth models, as well as associated perturbed models to ensure enough generalization capacity. We find that the UNet inversion is robust and has a better performance to reconstruct subsurface V-s distributions than the damping least-squares method, but at the expense of slightly higher data misfits. The pre-trained network can predict subsurface V-s models and associated uncertainties beneath NE of the Tibetan Plateau, which is consistent with the published models.