【摘要】：正New solution methods were considered for migration de-convolution in seismic imaging problems.It is well known that direct migration methods,using the adjoint operator L~*, yield a lower-resolution or blurred image,and that the linearized inversion of seismic data for the reflectivity model usually requires solving a(regularized) least-squares migration problem.We observed that the(regularized) least-squares method is computationally expensive,which becomes a severe obstacle for practical applications.Iterative gradient-descent methods were studied and an efficient method for migration deconvolution was developed.The problem was formulated by incorporating regularizing constraints,and then a nonmonotone gradient-descent method was applied to accelerate the convergence.To test the potential of the application of the developed method,synthetic two-dimensional and three-dimensional seismic-migration-deconvolution simulations were performed.Numerical performance indicates that this method is promising for practical seismic migration imaging.