云南预警台网异构波形数据集构建与震相拾取模型性能评估

吕帅; 房立华; 彭钰翔; 曹颖; 夏登科; 范莉苹; 朱杰; 郭亚茹

doi:10.3799/dqkx.2025.287

Volume 51 Issue 1

Jan. 2026

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Article Navigation > Earth Science > 2026 > 51(1): 74-89

Lyu Shuai, Fang Lihua, Peng Yuxiang, Cao Ying, Xia Dengke, Fan Liping, Zhu Jie, Guo Yaru, 2026. Development of a Heterogeneous Waveform Dataset and Evaluation of Phase Picking Models for Yunnan Earthquake Early Warning Network. Earth Science, 51(1): 74-89. doi: 10.3799/dqkx.2025.287

Citation:

Lyu Shuai, Fang Lihua, Peng Yuxiang, Cao Ying, Xia Dengke, Fan Liping, Zhu Jie, Guo Yaru, 2026. Development of a Heterogeneous Waveform Dataset and Evaluation of Phase Picking Models for Yunnan Earthquake Early Warning Network. Earth Science, 51(1): 74-89. doi: 10.3799/dqkx.2025.287

Citation:

Lyu Shuai, Fang Lihua, Peng Yuxiang, Cao Ying, Xia Dengke, Fan Liping, Zhu Jie, Guo Yaru, 2026. Development of a Heterogeneous Waveform Dataset and Evaluation of Phase Picking Models for Yunnan Earthquake Early Warning Network. Earth Science, 51(1): 74-89. doi: 10.3799/dqkx.2025.287

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Development of a Heterogeneous Waveform Dataset and Evaluation of Phase Picking Models for Yunnan Earthquake Early Warning Network

doi: 10.3799/dqkx.2025.287

Lyu Shuai^{1, 2
,
,},
Fang Lihua^{3
,
,},
Peng Yuxiang¹,
Cao Ying¹,
Xia Dengke⁴,
Fan Liping⁴,
Zhu Jie¹,
Guo Yaru¹

1.
Yunnan Earthquake Agency, Kunming 650224, China
2.
School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing 100083, China
3.
Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
4.
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China

Received Date: 2025-08-17
Publish Date: 2026-01-25

Abstract

Abstract

In recent years, deep learning methods have been widely applied to seismic detection and phase picking. However, existing models are mainly trained on high signal-to-noise ratio (SNR) velocity-type waveform data, with limited evaluation of their generalization to accelerometer and intensity meter data. To investigate the performance of existing models on accelerometer data and their generalization capability in Yunnan, this study constructed a high-quality, multi-source heterogeneous waveform dataset based on the latest observations from the Yunnan Earthquake Early Warning (EEW) network, including velocity meters, accelerometers, and intensity meters, with all phase arrival times manually annotated. It systematically evaluated the phase-picking performance of nine models-on the Yunnan dataset five domain-specific models (e.g., PhaseNet, USTC-Pickers) and four large models (e.g., SeisMoLLM, SeisT). The locally fine-tuned USTC-Pickers achieved the best overall performance, with mean F1 scores of 0.779 for Pg and Sg phase picking (Δt≤0.1 s), significantly outperforming other models, and effectively mitigating phase-picking delays for accelerometer and intensity meter data. Large models demonstrated stronger generalization in Sg picking and low-SNR conditions. The study also revealed performance variations of mainstream seismic detection models under different waveform lengths, magnitudes, and epicentral distances, underscoring the importance of localized training and model architecture selection in practical applications. The research findings provide references for seismic detection and phase picking in earthquake early warning systems, as well as for the real-time automatic processing of seismic data at the China Earthquake Science Experiment Site.
- seismic phase picking,
- dataset,
- large language model,
- performance evaluation,
- earthquake early warning,
- seismology

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References(32)

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