Open Access

Wei Li, Megha Chakraborty, Yu Sha, Kai Zhou, Johannes Faber, Georg Rümpker, Horst Stöcker, Nishtha Srivastava

• Highlights • We use 1D-ResNet-based architecture for seismic phase detection. • We report a 4% improvement in earthquake detection as compared to previous methods. • We show the model generalization ability on the STanford EArthquake Dataset (STEAD). • We show its robustness by masking phases of low magnitude with various noise levels. Abstract Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data has been achieved. This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods. In this study, we develop 1D deep Residual Neural Network (ResNet), for tackling the problem of seismic signal detection and phase identification. This method is trained and tested on the dataset recorded by the Southern California Seismic Network. Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases. Compared to previously proposed deep learning methods, the introduced framework achieves around 4% improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center. The model generalizability is also tested further on the STanford EArthquake Dataset. In addition, the experimental result on the same subset of the STanford EArthquake Dataset, when masked by different noise levels, demonstrates the model’s robustness in identifying the seismic phases of small magnitude.