In seismic exploration, regularized migration inversion of seismic data usually requires solving a weighted least-squares problem with constrains. It is well known that directly solving this problem using some decomposition techniques is very time-consuming, which makes it less possible for practical use. For iterative methods, previous research is mainly on solving the inverse model in a full space. In this paper, a robust subspace method is applied to seismic migration inversion with Gaussian

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... beam representations of Green's function. The problem is first formulated by incorporating regularizing constraints, and then, it is changed from full space to subspace and solved by a trust-region method. To test the potential of the application of the developed method, synthetic data simulations are performed. The results show that this method is very promising for ill-posed seismic migration inversion problems.