A combined denoising approach based on EEMD and sparse-constrained curvelet transform
Liu Xuetong, Lei Liu, Zhou Xuefeng, Wenbin Li · International Geophysical Conference, Beijing, China, 24-27 April 2018 · 2018
Considering the the shortcomings of empirical mode decomposition (EMD) method eliminate high frequency component directly and conventional curvelet threshold denoising method. This paper introduces sparse-constrained curvelet optimal iterative threshold denoising method based on ensemble empirical mode decomposition (EEMD).The EEMD method could solve the problem of mode mixing in empirical mode decomposition effectively. Besides, sparse-constrained curvelet transform optimal iterative threshold denoising method could improve the non-smooth distortion phenomenon and the problem of threshold selection by tradtional curvelet threshold method. Our proposed approach combines the advantages of above methods. Fisrtly, the noise signal is decomposed into a series of intrinsic mode functions (IMFs) with different levels of noise by EEMD method. Then sparse-contrained curvelet transform optimal iterative threshold method is designed to suppress the noise of IMFs. Finally, the denoising data is obtained by reconstructing the denoised IMFs. Model test results verify the effectiveness of the new approach, which could keep effective signal and improve S/N ratio effectively. Besides, the new approach could remove random noise and improve the identification precision of the volcano conduits effectively in the target oil field.