Regularized least-squares reverse time migration with prior model

Abstract

We introduce the prior model information to propose the regularized least-squares reverse time migration method (RLSRTM). The prior information is added into leastsquares reverse time migration (LSRTM) as the regularization term. The dynamic regularization parameter and scaling method to the regularization-term gradient is highlighted to ensure better constraint. To test the improvement of prior information to the inversion, LSRTM and RLSRTM is applied to a rough Marmousi model. The imaging results show that the prior information can improve the compensation to unbalanced illumination and precision of LSRTM. Moreover, the RLSRTM is more robust with low signal-to-noise ratio (SNR) data compared with the standard LSRTM.