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Open AccessArticle

IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method

1
School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
2
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
3
College of Earth Sciences, Guilin University of Technology, Guilin 541006, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 476; https://doi.org/10.3390/rs10030476
Received: 14 February 2018 / Revised: 11 March 2018 / Accepted: 15 March 2018 / Published: 19 March 2018
(This article belongs to the Special Issue Recent Advances in GPR Imaging)
Using traditional time-frequency analysis methods, it is possible to delineate the time-frequency structures of ground-penetrating radar (GPR) data. A series of applications based on time-frequency analysis were proposed for the GPR data processing and imaging. With respect to signal processing, GPR data are typically non-stationary, which limits the applications of these methods moving forward. Empirical mode decomposition (EMD) provides alternative solutions with a fresh perspective. With EMD, GPR data are decomposed into a set of sub-components, i.e., the intrinsic mode functions (IMFs). However, the mode-mixing effect may also bring some negatives. To utilize the IMFs’ benefits, and avoid the negatives of the EMD, we introduce a new decomposition scheme termed variational mode decomposition (VMD) for GPR data processing for imaging. Based on the decomposition results of the VMD, we propose a new method which we refer as “the IMF-slice”. In the proposed method, the IMFs are generated by the VMD trace by trace, and then each IMF is sorted and recorded into different profiles (i.e., the IMF-slices) according to its center frequency. Using IMF-slices, the GPR data can be divided into several IMF-slices, each of which delineates a main vibration mode, and some subsurface layers and geophysical events can be identified more clearly. The effectiveness of the proposed method is tested using synthetic benchmark signals, laboratory data and the field dataset. View Full-Text
Keywords: variational mode decomposition; empirical mode decomposition; IMF-slices; GPR data processing; GPR imaging; time-frequency analysis variational mode decomposition; empirical mode decomposition; IMF-slices; GPR data processing; GPR imaging; time-frequency analysis
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MDPI and ACS Style

Zhang, X.; Nilot, E.; Feng, X.; Ren, Q.; Zhang, Z. IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method. Remote Sens. 2018, 10, 476. https://doi.org/10.3390/rs10030476

AMA Style

Zhang X, Nilot E, Feng X, Ren Q, Zhang Z. IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method. Remote Sensing. 2018; 10(3):476. https://doi.org/10.3390/rs10030476

Chicago/Turabian Style

Zhang, Xuebing; Nilot, Enhedelihai; Feng, Xuan; Ren, Qianci; Zhang, Zhijia. 2018. "IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method" Remote Sens. 10, no. 3: 476. https://doi.org/10.3390/rs10030476

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