Abstract: Desert seismic recordings contain lots of complex noise which reduces signal-to-noise ratio. To solve this problem, an adaptive rank convergence denoising algorithm combining VMD ( Variational Mode Decomposition) with MoG-RPCA (Mixture of Gauss-Robust Principal Component Analysis) is proposed. The desert seismic data is firstly decomposed by VMD. All the decomposed modalities are rearranged into a new signal matrix, and then the matrix is subjected to low-rank decomposition by MoG-RPCA. When the error of decomposition satisfies the pre-determined requirement, the efficient low-rank component is extracted. Finally superimpose all the modalities of each channel signal in the low-rank matrix and substract from the original seismic data to achieve denoising. This method avoids choosing the modes of VMD and performs an adaptive rank convergence to the traditional low-rank decomposition. Simulation experiment and actual data processing show that the algorithm can effectively suppress low-frequency noise while maintaining more than 85% amplitude of the effective signal.

Key words: variational mode decomposition ( VMD), mixture of gauss-robust principal component analysis (MoG-RPCA), adaptive rank convergence, desert random noise, seismic exploration