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GPR data reconstruction method based on compressive sensing and K‐SVD
Author(s) -
Xu Juncai,
Shen Zhenzhong,
Tian Zhenhong
Publication year - 2018
Publication title -
near surface geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.639
H-Index - 39
eISSN - 1873-0604
pISSN - 1569-4445
DOI - 10.3997/1873-0604.2017030
Subject(s) - ground penetrating radar , singular value decomposition , compressed sensing , radar , missing data , sparse matrix , matrix (chemical analysis) , sampling (signal processing) , computer science , trace (psycholinguistics) , geology , algorithm , remote sensing , computer vision , machine learning , physics , chemistry , telecommunications , philosophy , filter (signal processing) , quantum mechanics , chromatography , gaussian , linguistics
Missing and irregular ground‐penetrating radar trace data resulting from sampling conditions are important issues in engineering. This study adopted compressive sensing theory to reconstruct missing ground‐penetrating radar trace data. A ground‐penetrating radar data reconstruction method was established based on compressive sensing theory and K‐singular value decomposition. The method used the sampling matrix of the missing data as the measurement matrix and the K‐singular value decomposition algorithm to obtain a complete dictionary of sparse coefficients. A traditional dictionary cannot be adaptively adjusted according to the data features; the proposed method resolved this problem. The iteratively reweighted least‐squares method was used to reconstruct the missing trace data. Two experiments on the recovery of missing ground‐penetrating radar data through a simulation and a field example were conducted to test the feasibility and effectiveness of the proposed method.