使用多式融合神经网络进行可扩展的中期地震预测
Yumeng Hu1, Qi Zhang2, Hengshu Zhu3,4
1School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China.
Scientific reports
|March 22, 2025
概括
深度学习框架SafeNet通过整合各种地震数据来增强地震预测. 这种新的方法在预测地震活动方面显示出卓越的性能和可扩展性.
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 人工智能的人工智能
背景情况:
- 地震学面临的挑战是整合各种各样的地震观测数据.
- 现有的工具很难有效地结合异构的地震信息.
研究的目的:
- 引入SafeNet,这是一个可扩展的深度学习框架,用于先进的地震数据集成和预测.
- 利用多式联接神经网络来改进地震模式识别.
主要方法:
- 开发了SafeNet,一个使用多式联接神经网络的框架.
- 集成的282维地震指标和地质地图.
- 采用专门的融合模块和适应性注意力机制来进行时空数据交换.
主要成果:
- 与13个最先进的模型相比,SafeNet在50年的中国目录测试中表现出优越的地震预测性能.
- 该框架成功地将在中国数据上训练的模型转移到美国,证明了其可扩展性.
结论:
- 安全网为集成复杂的地震数据提供了强大的解决方案.
- 该框架显示了在全球推进地震预测和理解方面的巨大潜力.
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