Fan Xuanmei, Wang Xin, Fang Chengyong, Jansen John D, Dai Lanxin, Tanyas Hakan, Zang Nan, Tang Ran, Xu Qiang, Huang Runqiu
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China.
GFÚ Institute of Geophysics, Czech Academy of Sciences, Prague 117 20, Czechia.
Natl Sci Rev. 2025 May 9;12(7):nwaf179. doi: 10.1093/nsr/nwaf179. eCollection 2025 Jul.
Earthquake-triggered (coseismic) landsliding is among the most lethal of disasters, and rapid response is crucial to prevent cascading hazards that further threaten lives and infrastructure. Current prediction approaches are limited by oversimplified physical models, regionally focused databases, and retrospective statistical methods, which impede timely and accurate hazard assessments. To overcome these constraints, we developed the first comprehensive global database of ∼400 000 landslides associated with 38 of the most catastrophic earthquakes over the past 50 years. Leveraging this extensive dataset, we developed advanced deep-learning models that predict the probability of landsliding for any earthquake worldwide with an average spatial accuracy of ∼82% in less than a minute, without relying on prior local knowledge. Our framework enables swift disaster evaluation during the critical early hours following an earthquake while also enhancing pre-event hazard planning. This study offers a scalable and efficient tool to mitigate the catastrophic impacts of earthquake-triggered landslides, representing a transformative advance in global geohazard prediction.
地震引发的（同震）山体滑坡是最致命的灾害之一，快速响应对于预防进一步威胁生命和基础设施的连锁灾害至关重要。当前的预测方法受到过于简化的物理模型、区域针对性的数据库和回顾性统计方法的限制，这阻碍了及时准确的灾害评估。为克服这些限制，我们开发了首个全面的全球数据库，涵盖过去50年里与38次最具灾难性地震相关的约40万起山体滑坡事件。利用这个广泛的数据集，我们开发了先进的深度学习模型，能够在不到一分钟的时间内预测全球任何地震引发山体滑坡的概率，平均空间准确率约为82%，且无需依赖先前的当地知识。我们的框架能够在地震后的关键早期迅速进行灾害评估，同时还能加强震前灾害规划。这项研究提供了一个可扩展且高效的工具，以减轻地震引发山体滑坡的灾难性影响，代表了全球地质灾害预测方面的变革性进展。
