Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

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 section, 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.

Particle Swarm Optimization-Based Variational Mode Decomposition for Ground Penetrating Radar Data Denoising

“…λ is Lagrange multipliers. A detailed description can be found in the related literature [11][12][13][14].…”

“…In this regard, Zhang et al applied the VMD method to GPR data processing, and both experimental and field-measured data showed that VMD−derived IMFs are sparser and match better with the signal's intrinsic properties than those derived from EMD denoising processing [12]. Xu and Lei also applied the VMD method to GPR signals with non-stationary characteristics and found that the VMD method was effective in removing noise from the GPR signal in a strong noise background and VMD was better at overcoming the influences of mode aliasing and endpoint effects compared to the traditional methods like EMD and EEMD [13]. He et al proposed a novel joint time−frequency analysis (JTFA) method based on the VMD for GPR data processing and verified that VMD outperforms the EMD, EEMD, and CEEMD in signal decomposition robustness and anti−noise ability by the synthetic signals, and the real−world field data [14].…”

Design Wave Height Parameter Estimation Model Reflecting the Influence of Typhoon Time and Space

“…According to the statistics of the Munich Re Group disaster database, in the six years from 2013 to 2018, 36,200 deaths were caused by global hydrological events with an economic loss of 213 billion USD, of which catastrophic hydrologic events accounted for about 56% [6]. In all catastrophic hydrological events, storm surges and huge waves caused by typhoons have severely threatened the survival and development of humankind with their frequent occurrences and formidable destructive power [7][8][9]. Establishing projects, such as wave and flood prevention seawalls, is an effective way to prevent the destruction of offshore constructions caused by typhoons (hurricanes) from marine dynamic environmental factors (wind, waves, etc.…”

Evaluating the Properties of Railway Ballast Using Spectral Analysis of Ground Penetrating Radar Signal Based on Optimized Variational Mode Decomposition

“…Recently, the variational mode decomposition (VMD), which encompasses multiple adaptive Wiener filter groups, has been widely used to process GPR signal, because it showed a good robustness in overcoming the disadvantages of mode aliasing, small end effects, and pseudocomponents when compared to other traditional TF analysis algorithms for GPR [13,14]. This innovative VMD method also has a solid theoretical foundation in decomposing the adaptive and quasiorthogonal signals [15].…”