A Lightweight Network for Seismic Phase Picking on Embedded Systems

Abstract

Phase picking is a critical task in seismic data processing, where deep learning methods have been applied to enhance its accuracy. While lightweight deep learning networks have been optimized for edge computing devices, there is a lack of networks developed explicitly for embedded systems. This paper presents a seismic phase picking model, a hybrid network integrating convolutional neural networks and Transformer, designed for embedded systems. Optimizing network parameters and computational resources, the model significantly reduces resource consumption while guaranteeing accuracy. It employs a multi-branch architecture. Specifically, the global branch employs a modified self-attention mechanism, effectively extracting global features through shared contextual information. The local branch retains local information from the input features. Such a multi-branch architecture facilitates effective interaction between global features and local details, thereby more efficiently capturing the relationships among features. The model can be configured into variants with different sizes to match various embedded systems. This research evaluated the model using the Stanford Earthquake Dataset, achieving a precision of 99.9% for the P-phase and 99.3% for the S-phase. On Raspberry Pi, the model reduced inference time by 58.1% compared to the earthquake transformer while maintaining comparable detection performance.

Keywords

• Phase picking
• lightweight network
• modified self-attention
• embedded systems