Iterative Reconstruction of 3D Seismic Data via Multiple Constraints

The core in iterative reconstruction of 3D seismic data is the choice of constraint method. Recently, there are two popular approaches to design such a constraint: sparsity-promoting transform using sparsity constraint and rank reduction method using rank constraint. While the sparsity-promoting transform enjoys the advantage of high efficiency, it lacks adaptivity to various data patterns. On the other hand, rank reduction method can be adaptively applied to different datasets but its computational cost is quite expensive. In this paper, we propose the multiple constraints based on a novel hybrid rank-sparsity constraint (HRSC) model which aims at combining the benefits of both approaches. Also, we design the corresponding HRSC framework to effectively solve the proposed new model via tightly combining sparsity-promoting transform and rank reduction method, which is more powerful in simultaneous reconstruction and denoising of 3D seismic data. The proposed HRSC framework aims to provide an extra level of constraint thus significantly improving the signal-to-noise ratio of the reconstructed results with high efficiency. Application of the HRSC framework on synthetic and field 3D seismic data demonstrates a superior performance compared with the famous rank reduction method, which is known as multichannel singular spectrum analysis (MSSA).