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