A Random Noise Suppression Method of Seismic Data Based on the VMD-ICA Algorithm
H. Meng, Qingyang Su, Hui Zeng, Xin Xu, H. Liu · 2021
Summary Seismic random noise has a serious impact on the resolution and signal-to-noise (S/N) ratio of seismic data. It can also cause unclear layers and structural artefacts in the seismic profile, which will affect the subsequent processing and interpretation effects. So it’s very important to remove the random noise effectively, especially in high-precision seismic data processing. However, for data with low S/N ratio, it’s difficult to separate the effective signal from the noise. In this paper, according to the time-frequency characteristics of seismic signals, a deniosing method based on variational mode decomposition (VMD) and independent component analysis (ICA) algorithm is proposed to suppress the random noise. Seismic signals can be decomposed into several mode components from high frequency to low frequency with a certain bandwidth by VMD, and combine the advantage of ICA that can extract the independent source signals. The noise and effective signal can be separated effectively, so that achieve the purpose of removing the random noise and improving the S/N ratio of seismic profile.