Seismic data reconstruction based on low dimensional manifold model

Seismic data reconstruction is an essential and yet fundamental step in seismic data processing workflow, which is of profound significance to improve migration imaging quality, multiple suppression effect, and seismic inversion accuracy. Regularization methods play a central role in solving the under-determined inverse problem of seismic data reconstruction. In this paper, a novel regularization approach is proposed, the low dimensional manifold model (LDMM), for reconstructing the missing seismic data. Our work relies on the fact that seismic patches always occupy a low dimensional manifold. Specifically, we exploit the dimension of the seismic patches manifold as a regularization term in the reconstruction problem, and reconstruct the missing seismic data by enforcing low dimensionality on this manifold. The crucial procedure of the proposed method is to solve the dimension of the patches manifold. Toward this, we adopt an efficient dimensionality calculation method based on low-rank approximation, which provides a reliable safeguard to enforce the constraints in the reconstruction process. Numerical experiments performed on synthetic and field seismic data demonstrate that, compared with the curvelet-based sparsity-promoting Ll-norm minimization method and the multi-channel singular spectrum analysis method, the proposed method obtains state-of-the-art reconstruction results. (C) 2021 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.