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Three-dimensional deformation field construction method for landslides using ICP algorithm and physics-informed deep learning
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DOI：10.1007/s11440-025-02918-y.png)
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摘要
En 中文
变形监测对于滑坡灾害的早期预警至关重要。构建三维（3D）变形场能够有效揭示滑坡的整体变形特征，从而提升监测和预警性能。本研究利用有限的离散监测数据，提出了一种新颖的滑坡3D变形场重构方法。应用改进的迭代最近点算法，对从地面激光扫描获取的多时相点云数据进行分块处理，得到坡面变形场。基于表面变形，采用平衡剖面算法初步估计潜在滑动深度。随后，利用物理信息深度学习模型整合有限的表面和地下变形数据，实现3D变形场的重构。与传统数值模拟方法和地质插值技术相比，所提方法利用现场实测数据进行建模，具有更高的精度和更简化的工作流程，便于对滑坡变形场进行有效的实时监测。重构的3D变形场有助于识别潜在破裂和滑动面，预测未监测区域的变形，并提供更清晰、更直观的观测结果。
Keyword:
Iterative closest point method
Multi-source monitoring
Physics-informed deep learning
Surface and subsurface deformation
Three-dimensional deformation field of landslides
期刊
IF:
5.7
论文数:
3.0K
被引数:
1.3W
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