ISSN 1004-4140
CN 11-3017/P
蔡育埼, 于子叶, 王伟涛, 等. 先导数据大模型:用于单台地震波形数据分析的双向神经网络预训练模型[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-6. DOI: 10.15953/j.ctta.2024.162.
引用本文: 蔡育埼, 于子叶, 王伟涛, 等. 先导数据大模型:用于单台地震波形数据分析的双向神经网络预训练模型[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-6. DOI: 10.15953/j.ctta.2024.162.
CAI Y Q, YU Z Y, AN Y R, et al. Bi-directional Pre-trained Network for Single-Station Seismic Waveform Analysis. DOI: 10.15953/j.ctta.2024.162
Citation: CAI Y Q, YU Z Y, AN Y R, et al. Bi-directional Pre-trained Network for Single-Station Seismic Waveform Analysis. DOI: 10.15953/j.ctta.2024.162

先导数据大模型:用于单台地震波形数据分析的双向神经网络预训练模型

Bi-directional Pre-trained Network for Single-Station Seismic Waveform Analysis.

  • 摘要: 机器学习特别是深度学习方法在地震学中的应用越来越广泛,其在震相检测、地震分类中都达到了接近人类的精度。但目前,多数地震学神经网络模型专注于单一任务。我们基于中国地震台网中心发布的CSNCD数据集构建了一个用于单台数据分析的双向神经网络预训练模型,模型以原始波形数据为输入,通过卷积神经网络和双向Transformer模型进行特征提取和处理,不仅可以完成常规的Pg、Sg、Pn震相检测、P波初动方向判定和事件类型判断工作,还可以通过迁移学习将模型用于其他地震波形数据分析工作中。

     

    Abstract: The application of machine learning, particularly deep learning methods, is becoming increasingly widespread in seismology, achieving near-human accuracy in tasks such as phase detection and event classification. However, most neural network models in seismology currently focus on single tasks. Based on the CSNCD dataset released by the China Earthquake Networks Center, we have developed a bi-directional neural network pre-trained model for single-station data analysis. This model uses three-component seismic waveform data as input and employs convolutional neural networks and bi-directional Transformer models for feature extraction and processing. It not only performs routine tasks such as Pg, Sg, and Pn phase detection, P-wave first-motion direction determination, and event type classification but can also be adapted to other seismic waveform data analysis tasks through transfer learning.

     

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