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GPR data noise attenuation on the curvelet transform

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Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its reflection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to filter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.

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Correspondence to Qian-Zong Bao.

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The research is supported by the National Natural Science Foundation of China (No. 41074089) and Special Financial Grant from the China Postdoctoral Science Foundation (No. 201104654).

Bao Qian-Zong received his M.S. in applied geophysics from Chang’an University in 2002 and his Ph.D. in Information and Communication Engineering from Xi’an Jiaotong University in 2009. In the same year, he joined the faculty of Chang’an University. His research interests include seismic and GPR signal analysis and processing, image processing, harmonic analysis, and signal sparse representation.

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Bao, QZ., Li, QC. & Chen, WC. GPR data noise attenuation on the curvelet transform. Appl. Geophys. 11, 301–310 (2014).

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