地震响应光谱的结构化生成建模,在过度波形几何学中的等级潜变量
1Department of Computer Science, University of Exeter, EX4 4RN, Exeter, United Kingdom.
Scientific reports
|January 7, 2026
概括
本研究介绍了一种对地震响应光谱的几何意识生成模型. 这种新的方法准确地预测了地震光谱,改善了地震危险分析和实时风险减轻.
科学领域:
- 地震学 地震学
- 机器学习 机器学习
- 几何深度学习 几何深度学习
背景情况:
- 准确预测地震响应光谱对于地震危险分析和结构评估至关重要.
- 传统的机器学习模型难以捕捉地震记录中的复杂的多尺度依赖关系和不确定性.
- 地震数据显示事件对事件和空间的变化,这给传统建模带来了挑战.
研究的目的:
- 为地震响应光谱提供一个具有几何意识的生成建模框架.
- 开发一种能够处理等级不确定性和多尺度依赖性的等级变异自编码器 (HVAE).
- 通过明确建模事件间和事件内部的变量,使得物理上一致的光谱幅度能够生成.
主要方法:
- 使用一个等级变量自编码器 (HVAE) 嵌入在波卡雷球式分散体中的潜在变量.
- 使用源和站点参数训练HVAE以规范潜伏空间.
- 利用过度的隐性几何学来提高表示效率和编码等级关系.
主要成果:
- 在强运动数据集上实现了高重建保真性,平均确定系数为0.961.
- 证明了物理上一致的光谱幅度的生成.
- 成功地将模型集成到随机地面运动模拟和预警系统中.
结论:
- 拟议的HVAE框架为地震响应光谱建模提供了一种原则性,与领域一致的方法.
- 这项工作将几何深度学习和地震模型结合起来,以改善地震风险减轻.
- 该模型的实际实用性可以通过其在实时地震危险评估和预警系统中的应用来证明.
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