Seismic inversion with L2,0-norm joint-sparse constraint on multi-trace impedance model
Ronghuo Dai, Jun Yang
Abstract
Abstract Impedance inversion of post-stack seismic data is a key technology in reservoir prediction and characterization. Compared to the common used single-trace impedance inversion, multi-trace impedance simultaneous inversion has many advantages. For example, it can take lateral regularization constraint to improve the lateral stability and resolution. We propose to use the L 2,0 -norm of multi-trace impedance model as a regularization constraint in multi-trace impedance inversion in this paper. L 2,0 -norm is a joint-sparse measure, which can not only measure the conventional vertical sparsity with L 0 -norm in vertical direction, but also measure the lateral continuity with L 2 -norm in lateral direction. Then, we use a split Bregman iteration strategy to solve the L 2,0 -norm joint-sparse constrained objective function. Next, we use a 2D numerical model and a real seismic data section to test the efficacy of the proposed method. The results show that the inverted impedance from the L 2,0 -norm constraint inversion has higher lateral stability and resolution compared to the inverted impedance from the conventional sparse constraint impedance inversion.