Low-Frequency Noise Suppression in Desert Seismic Data Based on an Improved Weighted Nuclear Norm Minimization Algorithm

Abstract

Noise suppression is a crucial step before seismic data analysis. The noise in desert areas has the characteristics of nonstationary, non-Gaussian, and low-frequency, which makes some traditional methods cannot suppress the noise well. The weighted nuclear norm minimization (WNNM), one of the most effective methods to suppress noise in all low-rank matrix approximation methods, assigns different weights to different singular values. The good denoising performance of the WNNM is based on the accuracy of block matching, and the theoretical basis of block matching is the nonlocal self-similarity of clean signals. However, the noise will destroy the nonlocal self-similarity and greatly affect the accuracy of block matching. In this article, to reduce the influence of noise on block matching, we use a bandpass filter to process each noisy block before block-matching and use the similarity between the filtered blocks to represent the similarity between the original noisy blocks, then stack the most similar noisy blocks to construct a matrix for further denoising. Experimental results demonstrate the efficiency of the proposed method for low-frequency noise suppression.