基于VMD-KSVD字典学习降噪的大坝变形预测  

Dam Deformation Prediction Based on VMD andKSVD Dictionary Learning Noise Reduction

作　　者：柳磊 李登华[2,3] 丁勇 LIU Lei;LI Denghua;DING Yong(School of Physics,Nanjing University of Science and Technology,Nanjing 210094,China;Nanjing Hydraulic Research Institute,Nanjing 210029,China;Key Laboratory of Reservoir Dam Safety,MWR,Nanjing 210024,China)

机构地区：[1]南京理工大学物理学院,南京市210094 [2]南京水利科学研究院,南京市210029 [3]水利部水库大坝安全重点实验室,南京市210024

出　　处：《大地测量与地球动力学》2024年第9期951-958,984,共9页Journal of Geodesy and Geodynamics

基　　金：国家重点研发计划(2022YFC3005502);国家自然科学基金(51979174,U2240221)。

摘　　要：提出一种自适应变分模态分解和KSVD字典学习相结合的降噪算法。该方法对监测序列分解后的子序列进行降噪,同时考虑残差序列的特征,从而充分保留监测序列中的有效信息。以某大坝变形监测数据为例进行测试,结果表明,该方法能够较好地保留监测序列中的有效信息,相较于传统的降噪算法更适用于复杂情况下的大坝变形预测,能进一步提高预测模型的泛化能力。This paper proposes a noise reduction algorithm combining adaptive variational mode decomposition and KSVD dictionary learning.In this method,we fully retain the effective information in the monitoring sequence by denoising the sub-sequences after the decomposition of the monitoring sequence,and consider the features in the residual sequence.We take the deformation monitoring data of a dam as an example.The results show that the proposed method can effectively retain the effective information in the monitoring sequence,and is more suitable for dam deformation prediction under complex conditions than the traditional noise reduction method,and can further improve the generalization ability of the prediction model.

关 键 词：自适应变分模态分解 KSVD 字典学习 变形预测 大坝安全监测 

分 类 号：P258[天文地球—测绘科学与技术]