通过条件生成模型学习地震的地面运动
Pu Ren1, Rie Nakata2,3,4, Maxime Lacour4,5
1Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. ren.pu@northeastern.edu.
Nature communications
|March 17, 2026
概括
人工智能 (AI) 谱图生成器,地面运动条件生成建模 (CGM-GM),可以预测地震地面运动. 这种人工智能工具对改善地震危险评估和基础设施弹性非常有希望.
科学领域:
- 地质物理学 地质物理学
- 人工智能的人工智能
- 地震学 地震学
背景情况:
- 预测地震地面运动对于地震危险评估和基础设施弹性至关重要.
- 目前的实证模拟和基于物理的模型等方法由于数据稀疏性,计算强度和复杂的地球结构要求而存在局限性.
研究的目的:
- 引入人工智能驱动的光谱图生成器,地面运动条件生成建模 (CGM-GM),用于高准确度的地面运动预测.
- 利用人工智能捕捉空间连续的里埃振幅光谱 (FAS) 和波形属性,而没有明确的物理约束.
主要方法:
- 使用概率自编码器提取隐藏的时间频率分布.
- 采用前后分布的变异顺序模型.
- 将地震强度和地理坐标输入到地面运动条件生成模型 (CGM-GM) 模型中.
主要成果:
- 基于地面运动条件生成建模 (CGM-GM) 模型成功捕获了空间连续的里埃振幅谱 (FAS).
- 该模型准确地预测了地震特性,如P和S到达和波形持续时间.
- 使用旧金山湾区地震记录进行评估,证明了有效的性能.
结论:
- 对地面运动的条件生成建模 (CGM-GM) 显示出作为对基于物理的模拟和实证地面运动模型的补充工具的潜力.
- 人工智能方法在地震学中为地震危险评估和基础设施弹性提供了有希望的进步.
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