Removal of 'strip noise' in radio-echo sounding data using combined wavelet and 2-D DFT filtering
Removal of 'strip noise' in radio-echo sounding data using combined wavelet and 2-D DFT filtering
复制标题
使用组合小波和二维 DFT 滤波去除无线电回波探测数据中的“带状噪声”
DOI：
10.1017/aog.2019.4
复制
发表时间：
2020
影响因子：
2.9
通讯作者：
Siegert Martin J.
中科院分区：
文献类型：
--
作者：
Wang Bangbing;Sun Bo;Wang Jiaxin;Greenbaum Jamin;Guo Jingxue;Lindzey Laura;Cui Xiangbin;Young Duncan A.;Blankenship Donald D.;Siegert Martin J.
无线电回声探测（RES）可用于了解冰盖过程、冰流结构和河床特性，使其成为冰川学勘探中最受欢迎的工具之一。然而，RES数据经常受到“条带噪声”的影响，这是由内部仪器噪声和干扰和/或外部环境干扰引起的，可能会妨碍测量和解释。例如，带状噪声可能导致冰床的功率降低，影响冰厚测量的质量和冰下条件的表征。本文提出了一种基于小波与二维傅立叶滤波相结合的条带噪声去除方法。首先，我们对RES数据进行离散小波分解，得到多尺度小波分量。然后，2-D离散傅里叶变换（DFT）频谱分析上包含的噪声成分进行。在傅立叶域中，条带噪声的二维DFT谱保持其线性特征，并且可以通过“目标掩蔽”操作来去除。最后，对所有小波分量（包括去除条带的分量）进行逆小波变换，以恢复具有增强保真度的数据。模型试验和实测资料处理表明，该方法能较好地去除条带噪声，同时也能去除冰面上较强的第一反射面，从而提高雷达资料的综合质量。
Radio-echo sounding (RES) can be used to understand ice-sheet processes, englacial flow structures and bed properties, making it one of the most popular tools in glaciological exploration. However, RES data are often subject to ‘strip noise’, caused by internal instrument noise and interference, and/or external environmental interference, which can hamper measurement and interpretation. For example, strip noise can result in reduced power from the bed, affecting the quality of ice thickness measurements and the characterization of subglacial conditions. Here, we present a method for removing strip noise based on combined wavelet and two-dimensional (2-D) Fourier filtering. First, we implement discrete wavelet decomposition on RES data to obtain multi-scale wavelet components. Then, 2-D discrete Fourier transform (DFT) spectral analysis is performed on components containing the noise. In the Fourier domain, the 2-D DFT spectrum of strip noise keeps its linear features and can be removed with a ‘targeted masking’ operation. Finally, inverse wavelet transforms are performed on all wavelet components, including strip-removed components, to restore the data with enhanced fidelity. Model tests and field-data processing demonstrate the method removes strip noise well and, incidentally, can remove the strong first reflector from the ice surface, thus improving the general quality of radar data.