Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
作者: Alireza Jafari, Geoffrey Fox, John B. Rundle, Andrea Donnellan, Lisa Grant Ludwig
分类: cs.LG, physics.geo-ph
发布日期: 2024-08-21
备注: 22 pages, 8 figures, 2 tables
💡 一句话要点
提出MultiFoundationQuake模型以解决地震实时预测问题
🎯 匹配领域: 支柱七：动作重定向 (Motion Retargeting) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 地震预测 时间序列分析 深度学习 多模态融合 时空关系 模型评估 基础模型
📋 核心要点
- 现有地震预测方法在对预训练基础模型和深度学习架构的评估上存在不足，缺乏全面的比较与分析。
- 论文提出了MultiFoundationQuake和GNNCoder两种创新方法，将地震预测视为时间序列预测问题，利用深度学习架构捕捉时空关系。
- 实验结果显示，MultiFoundationQuake模型在性能上优于其他定制架构，特别是在捕捉地震数据的时空特征方面表现突出。
📝 摘要（中文）
本论文旨在提升地震实时预测能力，针对现有文献中缺乏对预训练基础模型和现代深度学习架构的全面评估这一问题，提出了MultiFoundationQuake和GNNCoder两种创新方法。通过将地震预测视为时间序列预测问题，研究涵盖了1986年至2024年间南加州的地震数据。实验结果表明，所提出的模型在捕捉地震数据的时空关系方面优于其他定制架构，强调了数据集选择的重要性。
🔬 方法详解
问题定义：本论文旨在解决地震实时预测中的时空关系捕捉不足的问题。现有方法未能充分利用预训练基础模型和现代深度学习架构的优势，导致预测效果不佳。
核心思路：论文通过将地震预测视为时间序列预测问题，提出了MultiFoundationQuake和GNNCoder两种创新方法，旨在更好地捕捉地震数据的时空特征。
技术框架：整体架构包括数据预处理、模型训练和评估三个主要阶段。首先，对1986年至2024年间的地震数据进行处理，然后使用不同的深度学习架构进行训练，最后通过多种性能指标评估模型效果。
关键创新：最重要的技术创新在于引入了MultiFoundationPattern方法，该方法结合了定制模式与基础模型结果，作为辅助流进行处理，显著提升了预测性能。
关键设计：在模型设计中，采用了Nash-Sutcliffe效率和均方误差等关键性能指标进行评估，确保模型在不同空间区域的有效性。同时，模型的参数设置和损失函数设计经过精心调整，以优化预测效果。
🖼️ 关键图片
📊 实验亮点
实验结果表明，MultiFoundationQuake模型在捕捉地震数据的时空关系方面表现优异，相较于其他定制架构，其Nash-Sutcliffe效率和均方误差指标均有显著提升，展示了该模型在地震预测中的有效性和可靠性。
🎯 应用场景
该研究的潜在应用领域包括地震监测、灾害预警和城市规划等。通过提升地震预测的准确性，可以有效减少地震带来的人员伤亡和财产损失，具有重要的社会价值和实际意义。未来，该模型还可以扩展到其他自然灾害的预测与管理中。
📄 摘要（原文）
Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities remains a crucial and enduring objective aimed at reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive, long-term earthquake datasets. Despite significant advancements, existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures. These architectures, such as transformers or graph neural networks, uniquely focus on different aspects of data, including spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovation approaches called MultiFoundationQuake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California, spanning from 1986 to 2024. Earthquake time series is forecasted as a function of logarithm energy released by quakes. Our comprehensive evaluation employs several key performance metrics, notably Nash-Sutcliffe Efficiency and Mean Squared Error, over time in each spatial region. The results demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a new general approach termed MultiFoundationPattern that combines a bespoke pattern with foundation model results handled as auxiliary streams. In the earthquake case, the resultant MultiFoundationQuake model achieves the best overall performance.