Automatic Identification of Mantle Seismic Phases Using a Convolutional Neural Network

Abstract Typical seismic waveform data sets comprise hundreds of thousands to millions of records. Compilation is performed by time‐consuming handpicking of phase arrival times, or signal processing algorithms such as cross‐correlation. The latter generally underperform compared to handpicking. However, differences in picking methods creates variations in models and interpretation of Earth's structure. Here, we exploit the pattern recognition capabilities of Convolutional Neural Networks (CNN). Using a large handpicked data set, we train a CNN model to identify the seismic shear phase SS. This accelerates, automates, and makes consistent data compilation, a task usually completed by visual inspection and influenced by scientists' choices. The CNN model is employed to identify precursors to SS generated by mantle discontinuities. It identifies precursors in stacked and individual seismograms, producing new measurements of the mantle transition zone with quality comparable to handpicked data. This rapid acquisition of high‐quality observations has implications for automation of future seismic tomography studies.