Deep learning–based inverse analysis of GPR data for landslide hazards

Deep learning–based inverse analysis of GPR data for landslide hazards

In mountainous landscapes, the diverse geotechnical conditions amplify landslide susceptibility. Factors such as precipitation and seismic activity can trigger landslides, while inherent hazards such as voids, fissures, and compaction deficits jeopardize long-term slope stability. Detecting and fore...

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Bibliographic Details
Main Authors: Yulong Qin, Ze Jiang, Yongqiang Tian, Yuan Jiang, Guanyi Zhao, Jiang Yan, Zhentao Li, Ziwang Cui, Zihui Zhao, Linke Huang, Fuping Zhang, Junfeng Du, Zhongdi Rong
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Earth Science
Subjects:
landslide hazards
deep learning
inverse
forward simulation
neural networks
Online Access:https://www.frontiersin.org/articles/10.3389/feart.2023.1340484/full

Internet

https://www.frontiersin.org/articles/10.3389/feart.2023.1340484/full

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