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Online dictionary learning seismic weak signal denoising method under model constraints

LI Yong;ZHANG YiMing;LEI Qin;NIU Cong;ZHOU YuBang;YE YunFei;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Chengdu University of Technology;School of Geophysics,Chengdu University of Technology;CNOOC Research Institute Co.Ltd.;  
In this paper,the latest online dictionary learning denoising method is developed for the complexity of noise components and noise structures and the characteristics of weak signals.The online dictionary learning denoising is conducted by means of data-driven and iterative learning to obtain the sparse solution of the signal to realize the denoising of the signal.Based on this,an online dictionary learning denoising method under the combined constraints of datadriven and model-driven models is proposed.A better quality learning sample is obtained in a model driven process to build a dictionary and then to conduct denoising.Compared with the traditional wavelet transform for theoretical seismic synthesis recording,it is far superior to the traditional time-frequency domain denoising method in the case of low-SNR weak signals.The actual data denoising process shows that the online dictionary learning denoising method under model constraints is an effective denoising method.This joint denoising method can effectivelyextract weak signals against high noise and has broad application prospect.
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