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Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

1,2,* and 3
1
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
2
Department of Civil Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
3
Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2017; https://doi.org/10.3390/app9102017
Received: 10 April 2019 / Revised: 10 May 2019 / Accepted: 13 May 2019 / Published: 16 May 2019
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PDF [4565 KB, uploaded 16 May 2019]
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Abstract

Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert–Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert–Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method. View Full-Text
Keywords: ground penetrating radar; Hilbert-Huang Transform; empirical mode decomposition; variational mode decomposition; intrinsic mode function ground penetrating radar; Hilbert-Huang Transform; empirical mode decomposition; variational mode decomposition; intrinsic mode function
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Xu, J.; Lei, B. Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition. Appl. Sci. 2019, 9, 2017.

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