Least-squares reverse time migration with factorization-free priorconditioning

We present a least-squares reverse time migration (LSRTM) method using a factorization-free priorconditioning approach to overcome the low signal-to-noise (SNR) problem arising out of using severely undersampled data. Priorconditioning is a technique where the prior information is incorporated directly into the forward operator and into the solution space of the problem. The prior information that is used in this work is that the inverted reflectivity is sparse in the radon domain. The proposed method is factorization-free since the forward mapping is defined through the action of a sparse operator on a vector. The priorconditioning method is shown to produce reliable images with good SNR and free from aliasing artifacts when using very sparse shots for both synthetic and field data.