Least-squares reverse time migration based on unstructured gird

Compared with conventional reverse time migration, the least-squares reverse time migration can improve image quality by using the inversion-based algorithm. In addition, the accuracy and efficiency of this method mainly depends on the accuracy and efficiency of solving acoustic wave equation. The unstructured gird method describes interfaces more precisely and can use the velocity related adaptive mesh generation algorithm. In this paper, we propose a least squares reverse time migration algorithm based on unstructured gird. The purpose is to combine the advantages of the least-squares algorithm and unstructured gird method, and then to use Laplacian filter with compensation to remove the low-frequency noise in the calculation of gradient. Thus we can accelerate the speed of convergence of the objective function. This method is successfully applied to a simple inclined layered model and the Marmousi model, which demonstrates its efficiency and potential.