Global Geology 2019, 22(3) 199-208 DOI:   10.3969/j.issn.1673-9736.2019.03.07  ISSN: 1673-9736 CN: 22-1371/P

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Keywords
Ground Penetrating Radar
mode decomposition
IMF
mode-mixing
Authors
PubMed

Mode decomposition methods and their application in ground penetrating radar data processing

ZHOU Weifan1,2, ZENG Zhaofa1,2, LI Jing1,2

1. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China;
2. Ministry of Land and Resources Key Laboratory of Applied Geophysics, Changchun 130026, China

Abstract��

Ground Penetrating Radar (GPR) method is a widely used method in engineering geophysical exploration at home and abroad. Compared with other geological exploration methods, the GPR method has the advantages of faster detection, higher resolution, convenient operation and relatively low detection cost. With the wide application and continuous development of GPR methods, the processing and interpretation of GPR data is increasingly important. The authors introduce the development process and current situation of the modal decomposition method in processing GPR data, summarize the principles of four modal decomposition methods, and compare their advantages and disadvantages in ground penetrating radar data processing. The results show that when the quality of GPR data is good and the noise is small, Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods can be used for processing, whereas when the noise interference is large or the underground medium is complex, Complete Ensemble Empirical Mode Decomposition (CEEMD) and Variational Mode Decomposition (VMD) methods can be used for processing. The four modal decomposition methods have their own advantages and disadvantages in GPR data processing. At present, the processing of GPR data by CEEMD and VMD methods is the focus of research and discussion at home and abroad.

Keywords�� Ground Penetrating Radar   mode decomposition   IMF   mode-mixing  
Received 2019-06-06 Revised 2019-07-08 Online:  
DOI: 10.3969/j.issn.1673-9736.2019.03.07
Fund:
Corresponding Authors: ZENG Zhaofa
Email: zengzf@jlu.edu.cn
About author:

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