Application of adaptive iterative low-rank algorithm based on transform domain in desert seismic signal analysis

Abstract

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.