基于变分模态分解的探地雷达数据噪声抑制

Xuebing Zhang, Xuan Feng, E. Nilot, Minghe Zhang
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引用次数: 2

摘要

探地雷达(GPR)在土木工程和地球科学等诸多领域都有广泛的应用。而探地雷达数据的分析与噪声抑制一直是研究的热点。本文介绍了一种新的自适应时频分解工具——变分模态分解(VMD)。利用VMD方法推导出一组平稳子分量,在此基础上分离出有效信号和噪声对应分量。以一段探地雷达数据为例,验证了VMD分解的效果,并与经验模态分解(EMD)进行了比较。并提出了一种基于VMD方案的初级噪声抑制方法。实地探地雷达数据的应用进一步证明了该方法在噪声抑制和地球物理事件保留方面具有较好的效果。
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Noise suppression of GPR data using Variational Mode Decomposition
Ground penetrating radar (GPR) has been used in the many aspects, such as civil engineering and the earth sciences. And the analysis and noise suppression of GPR data have always been the research focus. In this study, a new self-adaptive time-frequency decomposition tool called the variational mode decomposition (VMD) is introduced. We use the VMD method to derive a set of stationary sub-components, and based on the decomposition, we separate the valid signals and the components which are corresponded to the noise. One trace of GPR data are given to test the effect of the VMD decomposition, and the empirical mode decomposition (EMD) is also employed as a comparison. And a primary noise-suppression method based on the VMD scheme is also proposed. The application of the field GPR data further demonstrates the better performance of the proposed method in both noise suppression and the retention of geophysical events.
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