SeismoQuakeGNN:一种混合框架,用于用变压器增强的模型进行时空地震预测
Anny Leema1, Ponnuraman Balakrishnan1, Gladys Gnana Kiruba1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Frontiers in artificial intelligence
|December 18, 2025
概括
准确的地震预测对于灾难准备至关重要. 一个新的混合图形神经网络 (GNN) 和变压器模型,SeismoQuakeGNN,通过整合空间和时间学习来提高地震预测的准确性.
科学领域:
- 地质物理学 地质物理学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 准确的地震预测对于灾难准备和风险减轻至关重要.
- 传统的机器学习模型在捕捉复杂的时空地震数据关系方面面临着挑战.
- 现有的模型经常与地震事件固有的复杂的空间和时间动态作斗争.
研究的目的:
- 推出SeismoQuakeGNN,这是一个结合图形神经网络 (GNN) 和变压器进行增强地震预测的新型混合框架.
- 为了解决当前模型在捕获复杂的时空地震数据方面的局限性.
- 为改进地震预测和危险评估开发一种可靠的方法.
主要方法:
- 开发了SeismoQuakeGNN,这是一个混合框架,集成了图形神经网络 (GNN) 和变压器架构.
- 实现了一个优化的空间编码机制,用于动态学习地震相互依赖.
- 整合了一个由变压器驱动的注意模块,以捕捉地震数据中的远程时间相关性.
主要成果:
- 与XGBoost等传统方法相比,GNN在学习地震模式方面表现优越.
- 该模型有效地处理空间和时间维度的复杂关系.
- 在地震预测准确性和在不同地震地区的可转移性方面取得了实质性的改进.
结论:
- 对于先进的地震预测和危险评估,GNN提供了一个可靠的起点.
- 混合GNN-变压器方法有效地模拟了复杂的时空地震动态.
- 该模型能够在地震地区进行概括,这突显了其在地震学中广泛应用的潜力.
关键词:
长期短期记忆 长期短期记忆地震地震GNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN图表神经网络的神经网络随机森林回归随机森林回归地震数据 地震数据更多相关视频
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