TranSeis: A high precision multitask seismic waveform detector

Abstract

This work introduces a highly efficient multitask parallel Artificial Intelligence model designed for weak seismic signal detection and phase picking, leveraging the capabilities of the conventional AI-powered Transformer architecture. By integrating a multi-part data extraction strategy, a multi-GPU parallel processing framework, and a multi-layer network schedule, we significantly enhance the accuracy of detecting P- and S-phases while optimizing the model's efficiency. The accuracy attained for the P and S phases was 92% and 76% when employing only a segment of the dataset. When we incorporated the entire dataset, the precision improved to 97% for P phases and 87% for S phases. Notably, our model demonstrates higher accuracy compared to existing deep-learning and traditional detection algorithms. When applied to extensive seismic phase observation data collected from 2020 to 2023 in mainland China, our model consistently demonstrated high accuracy, confirming its generalizability across various spatiotemporal contexts. It also exhibited exceptional sensitivity to subtle changes in waveform data, highlighting its promising potential for detecting smaller seismic events with even greater resolution in future applications.