Seismic Random Noise Separation and Attenuation Based on MVMD and MSSA

Abstract

Seismic noise separation and attenuation is a fundamental topic in the seismic signal processing and geological interpretation. Several kinds of algorithms are proposed for separating and attenuating seismic random noises. However, the conventional methods often handle a seismic volume trace by trace, which ignores the lateral continuity of seismic data. Moreover, seismic signal is a typical broadband signal, which makes it difficult to separate and attenuate random noises contained in the whole frequency bands of the raw seismic data. Additionally, the tuning parameters for the denoising methods are also a difficult task to filter a broadband seismic signal. In this study, we propose a multi-channel scheme which is referred as the multi-channel variational mode decomposition (MVMD) based on multi-channel singular spectrum analysis (MSSA), to efficiently and effectively separate and attenuate seismic random noises. The proposed workflow first adopts the MVMD to decompose a 2-D seismic data into several band-limited intrinsic mode functions (IMFs) with different center frequencies and different bandwidths. Then, we leverage the MSSA for each decomposed IMF to separate and attenuate random noises. It is worth to be noted that we can select the tuning MSSA parameters for each IMF based on their different center frequencies and bandwidths. Finally, we restore the valid seismic data by summing all filtered IMFs. Through detailed comparisons with the traditional denoising methods, the results from the synthetic examples and field data quantitatively and qualitatively demonstrate the effectiveness and accuracy of the proposed workflow for separating and attenuating seismic random noises.