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Data Processing and Interpretation of Antarctic Ice-Penetrating Radar Based on Variational Mode Decomposition

机译:基于变分分解的南极穿透冰雷达数据处理与解释

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摘要

In the Arctic and Antarctic scientific expeditions, ice-penetrating radar is an effective method for studying the bedrock under the ice sheet and ice information within the ice sheet. Because of the low conductivity of ice and the relatively uniform composition of ice sheets in the polar regions, ice-penetrating radar is able to obtain deeper and more abundant data than other geophysical methods. However, it is still necessary to suppress the noise in radar data to obtain more accurate and plentiful effective information. In this paper, the entirely non-recursive Variational Mode Decomposition (VMD) is applied to the data noise reduction of ice-penetrating radar. VMD is a decomposition method of adaptive and quasi-orthogonal signals, which decomposes airborne radar data into multiple frequency-limited quasi-orthogonal Intrinsic Mode Functions (IMFs). The IMFs containing noise are then removed according to the information distribution in the IMF’s components and the remaining IMFs are reconstructed. This paper employs this method to process the real ice-penetrating radar data, which effectively eliminates the interference noise in the data, improves the signal-to-noise ratio and obtains the clearer inner layer structure of ice. It is verified that the method can be applied to the noise reduction processing of polar ice-penetrating radar data very well, which provides a better basis for data interpretation. At last, we present fine ice structure within the ice sheet based on VMD denoised radar profile.
机译:在北极和南极的科学考察中,探冰雷达是研究冰盖下基岩和冰盖内冰信息的有效方法。由于极地地区冰的电导率低且冰盖的成分相对均匀,因此与其他地球物理方法相比,穿透冰的雷达能够获得更深,更丰富的数据。然而,仍然有必要抑制雷达数据中的噪声以获得更准确和丰富的有效信息。在本文中,将完全非递归的变分模式分解(VMD)应用于穿透冰雷达的数据降噪。 VMD是一种自适应和准正交信号的分解方法,它将机载雷达数据分解为多个频率受限的准正交本征函数(IMF)。然后,根据IMF组件中的信息分布,删除包含噪声的IMF,并重建其余的IMF。本文采用这种方法对真实的冰层雷达数据进行处理,有效消除了数据中的干扰噪声,提高了信噪比,使冰层更清晰。实践证明,该方法可以很好地应用于极地冰极雷达数据的降噪处理,为数据解释提供了较好的依据。最后,我们基于VMD去噪雷达剖面图介绍了冰盖内的精细冰结构。

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