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Near real-time spatial prediction of earthquake-induced landslides:A novel interpretable self-supervised learning method

引用

International Journal of Digital Earth 2023年第1期16卷 1885-1906页

作者： Xuewen Wang Xianmin Wang Xinlong Zhang Lizhe Wang Haixiang Guo Dongdong Li Hubei Subsurface Multi-scale Imaging Key Laboratory School of Geophysics and GeomaticsChina University of GeosciencesWuhanPeople’s Republic of China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China Key Laboratory of Geological and Evaluation of Ministry of Education China University of GeosciencesWuhanPeople’s Republic of China Laboratory of Natural Disaster Risk Prevension and Emergency Management School of Economics and ManagementChina University of GeosciencesWuhanPeople’s Republic of China College of Electronic Science and Engineering National University of Defense TechnologyChangshaPeople’s Republic of China

Near real-time spatial prediction of earthquake-induced landslides(EQILs)can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake;thus,EQIL prediction is very crucial to the 72-hour‘golden window’for *** work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau,a famous seismically-active zone,and proposes a novel interpretable self-supervised learning(ISeL)method for the near real-time spatial prediction of *** new method innovatively introduces swap noise at the unsupervised mechanism,which can improve the generalization performance and transferability of the model,and can effectively reduce false alarm and improve accuracy through *** interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial *** results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning ***,according to the interpretable module in the ISeL method,the critical controlling and triggering factors are *** ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

关键词： Coseismic landslide near real-time interpretable artificial intelligence self-supervised learning spatial distribution prediction

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