An Automated GPR Signal Denoising Scheme Based on Mode Decomposition and Principal Component Analysis

Ground penetrating radar (GPR) signal processing has used mode decomposition methods such as empirical mode decomposition (EMD), variational mode decomposition, and their variations, but these methods are typically constrained by manual mode selection for the extraction of real signals and unwanted noises. In this study, we propose an automatic mode selection scheme for GPR signal denoising, which is demonstrated by the combination of the improved complete ensemble EMD (ICEEMD) and the multiscale principal component analysis (MSPCA). For GPR B-scan data, the intrinsic mode functions (IMFs) are automatically screened using our predefined criteria to remove noise after their spectra and the relationship between signals are first analyzed. Next, the nonlinear and nonstationary signals are adaptively decomposed into many time series by the proposed automated ICEEMD. Each A-scan dataset is then screened and subjected to MSPCA before being reassembled into the final denoised result. The synthetic and real GPR data prove our denoising scheme's superiority over more established denoising techniques like MSPCA and median filtering.

Automated summary

This study proposes an automatic mode selection scheme for GPR signal denoising using the combination of improved complete ensemble EMD (ICEEMD) and multiscale principal component analysis (MSPCA). The scheme automatically screens the intrinsic mode functions (IMFs) of GPR B-scan data based on predefined criteria to remove noise. The adaptive ICEEMD decomposes the nonlinear and nonstationary signals into multiple time series, which are then subjected to MSPCA before being reassembled into the final denoised result. Synthetic and real GPR data demonstrate the superiority of this denoising scheme over established techniques like MSPCA and median filtering.