Seismic Impedance Inversion With Adaptive Group Sparse Regularization

Seismic impedance inversion, as a core technology in geophysical exploration, has evolved into a pivotal method for the quantitative prediction of subsurface rock elastic properties within exploration geophysics. Its accuracy and resolution directly impact the precision of hydrocarbon reservoir prediction and characterization. Seismic inversion often faces ill-posedness challenge. Sparse regularization, such as L1-norm, are frequently used to deal with this issue. But the noise interference is still mightiness when dealing with complex geological structures. It is because of inherent feature of sparse regularization which tends to suppress minor geological features. To compensate the limitations of conventional sparse regularization, this paper proposes an another regularization i.e., adaptive group sparse regularization. It is the generalization of the existing group sparse regularization and can adaptively find the optimal results without pre-defined group partition. Based on the theory of group sparse regularization, it can overcome the limitations of sparse regularization. We use adaptive group sparsity to regularize the seismic impedance inversion. To effectively solve the objective function, the variable splitting alternating iteration has been used. Through synthetic data test, it has been verified the feasibility of the proposed method. And then, through actual data tests, it has been verified the advantages in resolution enhancement in complex geological structures compared to the inversion method only with sparse regularization.