Multi-scale Attention-based Neural Network for Earthquake Phase Picking

Abstract

We propose a neural network for identifying P and S phase arrivals from seismic waveforms using a U-shaped architecture enhanced with multi-scale residual connections and attention mechanisms. Our experiments identify that challenges arise from a wide range of signal-to-noise ratios (S/N) and earthquake magnitudes. This is further exacerbated by a small training set, and related over-fitting issues. Our final metric is 9.383 sec2, which corresponds to the RMS error of 0.3468 sec.