Abstract
Ground-penetrating radar (GPR) is a kind of high-frequency electromagnetic detection technology. It is mainly used to locate targets and interfaces in underground structures. In addition to the effective signals reflected from the subsurface objects or interfaces, the GPR signals in field work also include noise and different clutters, such as antenna-coupled waves, ground clutters, and radio-frequency interference, which have similar wavelet spectral characteristics with the target signals. Clutter and noise seriously interfere with the target’s response signal. The singular value decomposition (SVD) filtering method can select appropriate singular values and characteristic components corresponding to the effective signals for signal reconstruction to filter the GPR data. However, the conventional time-domain SVD method introduces fake signals when eliminating direct waves, and does not have good suppression of random noise around non-horizontal phase axes. Here, an SVD method based on the Hankel matrix in the local frequency domain of GPR data is proposed. Different numerical models and real field GPR data were handled using the proposed method. Based on the power of fake signals introduced via different processes, qualitative and quantitative analyses were carried out. The comparison shows that the newly proposed method could improve efforts to suppress random noise around non-horizontal phase reflection events and weaken the horizontal fake signals introduced by eliminating clutter such as ground waves.
Highlights
Ground-penetrating radar (GPR) is a geophysical method using high-frequency electromagnetic waves to detect and locate targets and interfaces in underground structures [1]
Grzegorczyk proposed a two-pass GPR approach to detect recently buried nonmetallic scatters, whose signals were otherwise undistinguishable from surrounding clutter, using an algorithm based on a modified singular value decomposition approach, which was shown to perform significantly better than a direct signal cancellation approach [11]
Based on these research findings, this paper proposes an singular value decomposition (SVD) filtering method based on the Hankel matrix in the local frequency domain of GPR data to eliminate clutter and random noise
Summary
Ground-penetrating radar (GPR) is a geophysical method using high-frequency electromagnetic waves to detect and locate targets and interfaces in underground structures [1]. For seismic data, the conventional time-domain SVD method does not have good suppression of random noise around non-horizontal phase axes, and an improved SVD algorithm based on the Hankel matrix could deal with this problem. Based on these research findings, this paper proposes an SVD filtering method based on the Hankel matrix in the local frequency domain of GPR data to eliminate clutter and random noise. According to the characteristics of the GPR signal, the first few singular values correspond to components denoting the direct wave, reflected signals of the horizontal interface, and their multiple waves with strong linear correlation among the traces; and the smaller singular values correspond to components denoting the random noise with almost no correlation. The SVD method based on the Hankel matrix can improve the suppression of random noise around the non-horizontal phase axis and weaken the horizontal fake signals produced by eliminating the direct wave. The same process is repeated for every slice in the frequency domain; the inverse Fourier transform is used to get the noise-suppressed data in the time domain
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