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

doi:10.3799/dqkx.2025.287

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

doi: 10.3799/dqkx.2025.287

吕帅^1,2,
房立华^3, ,,
彭钰翔¹,
曹颖¹,
夏登科⁴,
范莉苹⁴,
朱杰¹,
郭亚茹¹

1. 云南省地震局, 云南昆明 650224;
2. 中国地质大学(北京), 北京 100083;
3. 中国地震局地震预测研究所, 北京 100036;
4. 中国地震局地球物理研究所, 北京 100081

基金项目:

地震科技星火计划项目(No.XH25033YB)和国家自然科学基金(No.42374081).

详细信息

作者简介:
吕帅(1991-),男,高级工程师,主要从事地震监测和地震信息化工作.E-mail：lv_303494@163.com.

通讯作者:
房立华(1981-),男,博士,研究员,主要从事人工智能地震学研究.E-mail：fanglihua@ief.ac.cn.

中图分类号: P315.61
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出版历程
- 收稿日期: 2025-08-17
- 网络出版日期: 2025-12-29

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

1. Yunnan Earthquake Agency, Kunming 650224, Yunnan, China;
2. School of Information Engineering, 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

摘要

摘要: 近年来,深度学习方法在地震检测和震相拾取中得到广泛的应用.然而,现有模型主要基于高信噪比的速度型波形数据进行训练,缺乏对加速度计与烈度计数据的泛化性评估.为探究现有模型对加速度数据的处理效果及在云南地区的泛化能力,本文基于云南预警台网的最新观测数据,构建了包括速度计、加速度计和烈度计的多源异构高质量波形数据集,且所有震相到时均由人工标注.结合PhaseNet、USTC-Pickers等五种专业模型,以及SeisMoLLM和SeisT等四种大模型,系统评估了不同模型在云南数据集上的震相拾取性能.结果表明：本地迁移优化的USTC-Pickers综合性能最优,其Pg和Sg震相拾取的平均F1值达0.779(到时拾取差异△t≤0.1 s),显著优于其他模型,且在检测加速度计与烈度计数据时,较好解决了震相拾取滞后问题;大模型在Sg拾取等复杂环境中展现出更强的泛化能力.研究还揭示了主流地震检测模型在不同波形长度、震级、震中距条件下的性能变化,强调了本地化训练与模型选取在实际应用中的重要性.研究结果为地震预警系统中的地震检测和震相识别,以及中国地震科学实验场地震观测数据的实时自动处理提供参考.
- 震相拾取 /
- 异构波形数据集 /
- 大模型 /
- 性能评估 /
- 地震预警
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. We systematically evaluated the phase-picking performance of nine models—five domain-specific models (e.g., PhaseNet, USTC-Pickers) and four large models (e.g., SeisMoLLM, SeisT)—on the Yunnan dataset. 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

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参考文献(32)

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