TranSeis: A high precision multitask seismic waveform detector

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. • A highly accurate and efficient AI-powered model with a refined and optimized architecture. • The multitask parallel model based on the Transformer architecture for seismic signal detection and phase picking. • The model is evaluated using the regional artificial intelligence (AI) earthquake dataset and real-time continuous waveform data collected in this study. • The scientific contribution of our model primarily lies in its enhanced accuracy and efficiency in seismic monitoring and genaralisation over mainlan of Chinaover. • New metric offers a reasonable assessment of model performance, emphasizing the need to normalize metrics in future studies.