结合短时傅里叶变换与注意力的震相识别模型

雷鸣; 周云耀; 向涯; 吕永清

doi:10.3799/dqkx.2025.086

Volume 51 Issue 1

Jan. 2026

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Lei Ming, Zhou Yunyao, Xiang Ya, Lyu Yongqing, 2026. A Phase Picking Model Integrating Short-Time Fourier Transform and Multi-Scale Attention. Earth Science, 51(1): 104-115. doi: 10.3799/dqkx.2025.086

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Lei Ming, Zhou Yunyao, Xiang Ya, Lyu Yongqing, 2026. A Phase Picking Model Integrating Short-Time Fourier Transform and Multi-Scale Attention. Earth Science, 51(1): 104-115. doi: 10.3799/dqkx.2025.086

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A Phase Picking Model Integrating Short-Time Fourier Transform and Multi-Scale Attention

doi: 10.3799/dqkx.2025.086

Lei Ming^{1, 2
,
,},
Zhou Yunyao^{1, 2, 3, 4
,
,},
Xiang Ya^{1, 2, 3},
Lyu Yongqing^{1, 2, 3}

1.
Wuhan Institute of Earth Observation, China Earthquake Administration, Wuhan 430071, China
2.
Institute of Seismology, China Earthquake Administration, Wuhan 430071, China
3.
Key Laboratory of Earthquake Geodesy, China Earthquake Administration, Wuhan 430071, China
4.
Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station, Wuhan 430071, China

Received Date: 2025-02-17
Publish Date: 2026-01-25

Abstract

Abstract

Seismic phase picking is a critical task in earthquake monitoring, as its accuracy directly impacts the precision of hypocenter localization and magnitude estimation. However, traditional methods are often limited in their ability to capture the characteristics of complex seismic signals. This study proposes a dual-branch deep learning model that integrates a multi-scale attention mechanism and short-time Fourier transform (STFT). The model extracts temporal features through a time-domain branch and captures time-frequency representations via a frequency-domain branch, while leveraging the attention mechanism to enhance multi-scale features. Experimental results show that within a 100 ms error threshold, the proposed model achieves a P-wave picking precision and recall of 95.69% and 88.97%, and an S-wave precision and recall of 87.98% and 77.25%, respectively. The mean and standard deviation of arrival time error for the P-wave are 18.76 ms and 27.13 ms, while for the S-wave they are 25.97 ms and 36.14 ms. Moreover, the model contains only 0.35 M parameters and incurs a computational cost of 71.38 M FLOPs. Compared with existing models, the SEN model not only achieves competitive performance but also demonstrates advantages in model size and computational efficiency, offering great potential for real-time seismic monitoring applications.
- seismic phase picking,
- deep learning,
- convolutional neural network,
- attention mechanism,
- time series,
- seismology

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References(38)

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