Elimination of random noise is crucial in seismic data processing. Especially in desert area, field record generally has problems of weak effective reflection wave and strong noise due to its special surface factors. Besides, desert noise has characteristics of low-frequency, non-stationary and non-Gaussian. Thus, it is difficult to separate the effective signal from desert noise in low-frequency band. In order to solve these problems, this paper proposes an iterative low-rank denoising method based on synchrosqueezed wavelet transform (SWT). The algorithm first transforms seismic signal into time–frequency domain by SWT, then the signal is decomposed by iterative low-rank decomposition. Different from a traditional low-rank algorithm, this paper performs an adaptive iterative convergence on low-rank decomposition algorithm. When the error of decomposition reaches the predetermined range, the effective low-rank component is extracted. In the end, the low-rank matrix is converted back to time domain by inverse SWT to achieve the denoising. The results of the synthetic and field records verify the effectiveness of the proposed method so that it can be applied to the denoising of desert seismic data. In addition, the surface waves in real desert seismic record have obvious suppression effects and the advantages of the algorithm are shown in the comparison experiments.

中文翻译：

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基于变换域的自适应迭代低秩算法在沙漠地震信号分析中的应用

在地震数据处理中，消除随机噪声至关重要。特别是在沙漠地区，由于其特殊的表面因素，实地记录通常存在有效反射波弱和噪声强的问题。此外，沙漠噪声具有低频，非平稳和非高斯的特征。因此，难以将有效信号与低频频带的沙漠噪声分离。为了解决这些问题，本文提出了一种基于同步压缩小波变换（SWT）的迭代低秩降噪方法。该算法首先通过SWT将地震信号转换到时频域，然后通过迭代低秩分解对信号进行分解。与传统的低秩算法不同，本文对低秩分解算法进行了自适应迭代收敛。当分解误差达到预定范围时，提取有效的低阶分量。最后，逆SWT将低秩矩阵转换回时域以实现去噪。综合和现场记录的结果验证了该方法的有效性，可用于沙漠地震数据的去噪。另外，真实沙漠地震记录中的地表波具有明显的抑制作用，比较实验表明了该算法的优点。综合和现场记录的结果验证了该方法的有效性，可用于沙漠地震数据的去噪。另外，真实沙漠地震记录中的地表波具有明显的抑制作用，比较实验表明了该算法的优点。综合和现场记录的结果验证了该方法的有效性，可用于沙漠地震数据的去噪。另外，真实沙漠地震记录中的地表波具有明显的抑制作用，比较实验表明了该算法的优点。