Automatically Extracting Surface-Wave Group and Phase Velocity Dispersion Curves from Dispersion Spectrograms Using a Convolutional Neural Network

Abstract To image high-resolution crustal and upper-mantle structures, ambient noise tomography (ANT) has been widely used on local and regional dense seismic arrays. One of the key steps in ANT is to extract surface-wave group and phase velocity dispersion curves from cross-correlation functions of continuous ambient noise recordings. One traditional way is to manually pick the dispersion curves from dispersion spectrograms in the period-velocity domains, which is very labor intensive and time consuming. Another way is to automatically pick the dispersion curves using some predefined criteria, which are not reliable in many cases especially for phase velocity data. In this study, we propose a novel method named DisperPicker to automatically extract fundamental mode group and phase velocity dispersion curves using a convolutional neural network (CNN). The inputs to CNN include paired group and phase velocity dispersion spectrograms in the period-velocity domains, which are calculated from empirical surface-wave Green’s functions. In this way, group velocity dispersion curves can implicitly guide the extraction of phase velocity dispersion curves, which have large ambiguities to pick on the dispersion spectrograms. The labels or outputs of the network are the probability images converted from dispersion curves. The U-net architecture is adopted because it is powerful for image processing. We have assembled short-period surface-wave data from three different dense seismic arrays to train the network. The trained network is further tested and validated by two datasets close to Chao Lake, China. The tests show that DisperPicker has the generalization ability to efficiently and accurately extract dispersion curves of large datasets without new training.