Offshore earthquakes and subsequent tsunamis pose significant risks to many coastal populations worldwide. This paper introduces a data‐driven machine learning model that synthesizes accurate 3D earthquake–tsunami fragility surfaces from randomly selected 2D fragility curves. The integration of physics‐based simulations enhances the model's reliability for these specific hazards, making it a valuable tool for multi‐hazard analysis in earthquake–tsunami contexts. Additionally, by shifting 2D fragility curves to represent retrofitted structural systems, the model can generate earthquake–tsunami fragility surfaces for community‐level mitigation studies. While the model is demonstrated for earthquake–tsunami scenarios, its methodology architecture has the potential to contribute to other multi‐hazard situations for the initial conditions in multi‐hazard community resilience analysis.

中文翻译：

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地震韧性分析中多灾害脆性表面的数据驱动型机器学习


近海地震和随之而来的海啸对全球许多沿海人口构成了重大风险。本文介绍了一种数据驱动的机器学习模型，该模型从随机选择的 2D 脆性曲线中合成精确的 3D 地震-海啸脆性表面。基于物理的仿真的集成增强了模型对这些特定灾害的可靠性，使其成为地震-海啸背景下多灾害分析的宝贵工具。此外，通过移动 2D 脆弱性曲线来表示改造后的结构系统，该模型可以生成地震-海啸脆弱性表面，用于社区层面的缓解研究。虽然该模型是针对地震-海啸情景进行的演示，但其方法架构有可能为多灾害社区弹性分析的初始条件的其他多灾害情况做出贡献。