Predicting geohazard runout is critical for protecting lives, infrastructure and ecosystems. Rapid mass flows, including landslides and avalanches, cause several thousand deaths across a wide range of environments, often travelling many kilometres from their source. The wide range of source conditions and material properties governing these flows makes their runout difficult to anticipate, particularly for downstream communities that may be suddenly exposed to severe impacts. Accurately predicting runout at scale requires models that are both physically realistic and computationally efficient, yet existing approaches face a fundamental speed-realism trade-off. Here we train a machine learning model to predict geohazard runout across representative real world terrains. The model predicts both flow extent and deposit thickness with high accuracy and 100 to 10,000 times faster computation than numerical solvers. It is trained on over 100,000 numerical simulations across over 10,000 real world digital elevation model chips and reproduces key physical behaviours, including avulsion and deposition patterns, while generalizing across different flow types, sizes and landscapes. Our results demonstrate that neural emulation enables rapid, spatially resolved runout prediction across diverse real world terrains, opening new opportunities for disaster risk reduction and impact-based forecasting. These results highlight neural emulation as a promising pathway for extending physically realistic geohazard modelling to spatial and temporal scales relevant for large scale early warning systems.

翻译：预测地质灾害的运移范围对于保护生命、基础设施和生态系统至关重要。包括滑坡和雪崩在内的快速物质流动，每年在全球多种环境中造成数千人死亡，其流动距离常可达源头数公里之遥。由于控制这些流动的源区条件与材料性质差异巨大，其运移范围难以预测，尤其对可能突然遭受严重冲击的下游社区而言更是如此。要实现大规模精准运移预测，需要兼具物理真实性与计算效率的模型，但现有方法始终面临速度与真实性的根本性权衡。本研究训练了一种机器学习模型，用于在具有代表性的真实地形上预测地质灾害运移。该模型能以比数值求解器快100至10,000倍的计算速度，高精度预测流动范围与堆积厚度。模型基于超过10,000个真实世界数字高程模型图块上的10万余次数值模拟进行训练，能够复现分流与沉积模式等关键物理行为，并泛化至不同流动类型、规模及地貌环境。我们的结果表明，神经仿真技术能够实现跨多样真实地形的快速空间解析运移预测，为灾害风险削减与基于影响的预报开辟了新途径。这些成果凸显了神经仿真作为拓展物理真实性地质灾害建模的可行路径，使其能够覆盖大规模预警系统所需的空间与时间尺度。