Low-Frequency Noise Suppression of Desert Seismic Data Based on Variational Mode Decomposition and Low-Rank Component Extraction

Abstract

In desert seismic records, random noise with complex characteristics such as nonstationary, non-Gaussian, nonlinear, and low-frequency will contaminate effective signals, which will greatly reduce the continuity and resolution of the seismic events. In order to achieve the requirements of high signal-to-noise ratio (SNR), high resolution, and high fidelity of seismic records after denoising, and to obtain high quality seismic exploration data, we present a method for suppressing low-frequency noise of desert seismic data, which combines variational mode decomposition (VMD) with low-rank matrix approximation algorithm. This method can further avoid the effect of spectrum aliasing on the denoising results because of using VMD. At first, the proposed method decomposes seismic signals into different modes by VMD and then arranges all modes into a signal matrix. An algorithm named OptShrink is used to extract the low-rank noise components, and great denoising effect is achieved by making a difference between the low-rank noise components and the original seismic record. The method is applied to synthetic desert seismic data and real desert seismic data. The experimental results show that the denoising effect of this method is better than that of previous methods in desert low-frequency noise. The effective signal remains intact, the resolution and continuity of the seismic events are improved obviously. The suppression of surface wave is also very thorough.