Desert seismic noise suppression based on an improved low-rank matrix approximation method

Abstract

The suppression of random noise is a crucial step before seismic data analysis. Random noise in desert areas has the characteristics of low frequency and non-stationary, and there is serious spectrum aliasing between random noise and effective signals, which makes it difficult to suppress such noise. In recent years, some methods based on signal rank minimization have achieved remarkable results in seismic random noise suppression. Since the implementation of low rank matrix approximation is an iterative process, noise estimation is an indispensable step before each iteration, but also an important step. The noise estimation method previously used is to calculate the residuals of the original noisy patch data and the corresponding iterative denoising version, which is intuitively considered as the filtered noise. This method may be very inaccurate in the case of high noise levels or complex seismic records. In this paper, a noise estimation method based on geometric texture is introduced to estimate the noise level by selecting weak textured patches in all seismic texture patches. At the same time, we reduce the loss of effective signals by truncating the singular values in each iteration. Experiments on both synthetic and field seismic data show that this method has better effect on suppressing random noise in desert areas.