Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Geophysical Journal International

DOI: 10.1093/gji/ggaf453

Authors: Xingguo Huang, Yupeng Zhang

Institution: Jilin University

Language: English

Topic: Seismology and Earthquake Studies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,416

Funders: National Natural Science Foundation of China, National Science and Technology Major Project