MSEP-TFormer: a multitask source estimation parameter transformer network for earthquake monitoring

SUMMARY Earthquake monitoring plays a critical role in disaster warning and geophysical research, including earthquake phase picking and source parameter estimation. However, traditional methods suffer from cumbersome workflow and challenges in parameter selection during multiparameter joint estimation. Here, we propose a multitask network for earthquake monitoring that reduces computational complexity by replacing the self-attention mechanism with fast Fourier transform. Through the integration of a fusion module and an enhancement module, the interaction between tasks is strengthened to optimize network performance. Additionally, a dynamic adaptive weight allocation strategy is introduced to achieve a balance among different tasks. The proposed method was trained and tested on the STEAD and INSTANCE data sets and compared with advanced approaches. The results demonstrate that this method outperforms other deep learning methods in earthquake phase picking and source parameter estimation, achieving lower errors and higher evaluation metrics, thus showing potential for practical application in earthquake monitoring.