用物理启发的生成模型，秒级合成百万级地震动时程。
Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
- 基于物理先验的潜在流匹配框架，实现高效生成
- 900万网格点上秒级生成，速度提升1万倍
- 适合地震风险评估与分布式基础设施设计
地震灾害分析与空间分布基础设施（如电网、能源管网）设计需要具备真实频谱特性和时空一致性的场景化地震动时程。然而，传统物理模拟生成不确定性量化所需的大量样本计算成本过高，难以用于工程流程。为此，我们提出物理启发的地震动流（GMFlow）框架，可在物理参数条件下生成大规模、真实的区域地震动时程。在旧金山湾区模拟地震场景中验证，GMFlow可在数秒内生成超过900万网格点的时空一致地震动，相比传统模拟流程提速10,000倍，为分布式基础设施的快速、不确定性感知灾害评估开辟路径。更广泛地，该方法推动了无网格函数生成建模的发展，或可拓展至多科学领域的大规模时空物理场合成。
原文摘要 · Abstract (English)
Earthquake hazard analysis and design of spatially distributed infrastructure, such as power grids and energy pipeline networks, require scenario-specific ground-motion time histories with realistic frequency content and spatiotemporal coherence. However, producing the large ensembles needed for uncertainty quantification with physics-based simulations is computationally intensive and impractical for engineering workflows. To address this challenge, we introduce Ground-Motion Flow (GMFlow), a physics-inspired latent operator flow matching framework that generates realistic, large-scale regional ground-motion time-histories conditioned on physical parameters. Validated on simulated earthquake scenarios in the San Francisco Bay Area, GMFlow generates spatially coherent ground motion across more than 9 million grid points in seconds, achieving a 10,000-fold speedup over the simulation workflow, which opens a path toward rapid and uncertainty-aware hazard assessment for distributed infrastructure. More broadly, GMFlow advances mesh-agnostic functional generative modeling and could potentially be extended to the synthesis of large-scale spatiotemporal physical fields in diverse scientific domains.
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。