We first demonstrate our works on multi-dimensional seismic data reconstruction with multiple constraints, then we provide some perspectives and insights on this framework from our experience, especially the new combinations and further applications of multiple constraints when facing complicated seismic data. The core in seismic reconstruction 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 abstract, we propose multiple constraints for seismic data reconstruction 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 algorithmic framework to effectively solve the proposed new model. Application of the HRSC framework on synthetic and field seismic data demonstrates a superior performance compared with the well-known multi-channel singular spectrum analysis (MSSA).
Presentation Date: Wednesday, October 19, 2016
Start Time: 10:20:00 AM
Location: 166
Presentation Type: ORAL
