Multi-sources Least-Squares Reverse Time Migration Based on T-distribution with Sparse Constrained Using K-SVD Dictionary Learning

Compared with the conventional migration method, least-squares reverse time migration (LSRTM) has a lot of advantages, such as higher imaging resolution, amplitude preservation and amplitude balance and so on. But, the L2 norm based LSRTM algorithm is very sensitive to noise, especially when the data contains outliers. Compared to L2 norm, Student’s t distribution has better robustness. We extend the Student's t distribution to the LSRTM algorithm. In addition, multi-sources inversion is an effective way to reduce the computational cost. However, it often generates crosstalk noise. In this paper, we use L1 norm sparse regularization constraints with K-SVD dictionary learning to suppress the crosstalk noise caused by phase encoding. Theoretical models and field data processing verify the effectiveness of the algorithm and suitability for complex models. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Start Time: 3:30 PM Location: Poster Station 10 Presentation Type: Poster