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

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 13; p. 2973
• Main Authors: Liu, Sixin, Chen, Yuhan, Luo, Chaopeng, Jiang, Hejun, Li, Hong, Li, Hongqing, Lu, Qi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 22.06.2022
• Subjects: Algorithms; Bandwidths; Data processing; Decomposition; geophysics; Ground penetrating radar; Ground Penetrating Radar (GPR); humans; Intrinsic Mode Function (IMF); Lagrange multiplier; Methods; Optimization; Parameters; Particle swarm optimization; Particle Swarm Optimization (PSO); Radar; Radar data; Reconstruction; Remote sensing; root mean square error (RMSE); Root-mean-square errors; Signal processing; Signal to noise ratio; signal-to-noise ratio (SNR); Variational Mode Decomposition (VMD)
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14132973

Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD algorithm determines the central frequency and bandwidth of each Intrinsic Mode Function (IMF) by iteratively searching for the optimal solution of the variational mode and is capable of adaptively and effectively dividing the signal in the frequency domain into the many IMFs. However, the penalty parameter α and the number of IMFs K in VMD processing are determined depending on manual experience, which are important parameters affecting the decomposition results. In this paper, we propose a method to automatically search the parameters α and K optimally by Particle Swarm Optimization (PSO) algorithm. Then the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are used to judge the best superposition of the IMFs for data reconstruction, and the process is data-driven without human subjective intervention. The proposed method is used to process the field data, and the reconstruction data show that this innovative VMD processing can effectively improve the SNR and highlight the target reflections, even some targets not found in pre-processing are also revealed.