1. The Engineering & Technical College of Chengdu University of Technology,Leshan 614000,China; 2. College of Geophysics,Chengdu University of Technology,Chengdu 610059,China
High-resolution seismic inversion is confronted with two problems:First,seismic inversion is an ill-posed problem and has multiplicity of solutions,and second,noise and distortion are generated in the flows of acquisition and processing to reduce the stability of the inversion algorithm.Aimed at solving these two problems, this paper proposes an inversion method of seismic reflectivity based on L1-L1-norm sparse representation.Firstly,the L1-norm regularization term is used to reduce the inversion multiplicity,and then the L1-norm fitting term is used to enhance the noise robustness.The wavelet is extracted by well logging and seismic data to construct the over-complete wedge wavelet dictionary,and then the seismic signal is sparsely decomposed by the L1-L1-norm sparse representation,so as to realize the high-resolution reflectivity inversion.The experimental results of wedge model and actual seismic data show that the inversion algorithm is stable and has good noise robustness,and the inversion results are accurate and credible through logging data calibration.
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