用生成式AI实现实时海啸淹没概率预测，提升预警可靠性
Real-time probabilistic tsunami forecasting via generative AI
- 基于条件扩散模型构建概率性海啸淹没预测框架
- 2011年东日本大地震数据验证，准确预测淹没深度与范围
- 首次实现不确定性随时间动态衰减的实时概率预警
明确的岸上海啸淹没预测可提升公众风险意识，但在近场巨型逆冲地震等高度不确定条件下，确定性预测的淹没边界可能错误地暗示安全区域。因此，当前预警主要关注海岸水位高度，而非岸上淹没情况。尽管机器学习可实现即时淹没预测，但其仍为确定性输出，缺乏不确定性量化。本文基于条件扩散模型（一种生成式AI）开发了一种概率集成模型，兼顾精度与校准性。利用2011年东日本大地震数据验证，模型能真实追踪震后不确定性随时间下降的过程，同时准确预测淹没深度与范围。该框架表明，生成式AI可推动海啸预警从确定性迈向概率化，为下一代早期预警系统奠定基础。
原文摘要 · Abstract (English)
Explicit onshore tsunami inundation forecasting can improve public risk awareness, but deterministically predicted inundation boundaries under highly uncertain conditions, such as near-field tsunamis generated by megathrust earthquakes, may falsely imply safety outside the boundaries. Consequently, current warnings primarily target coastal tsunami height, not onshore inundation. Although machine learning enables instant inundation predictions, they remain deterministic, lacking uncertainty quantification. Here, we develop a probabilistic ensemble model based on a conditional diffusion model (a type of generative AI) that reconciles accuracy with calibration. Validated with the 2011 Tohoku-oki earthquake data, our model faithfully tracks the postearthquake uncertainty decreasing over time while accurately predicting inundation depth and extent. Our framework shows that generative AI can shift tsunami forecasting from determinism to probabilism, providing a foundation for next-generation early warning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。