Reconstruction of seismic data with missing traces based on local random sampling and curvelet transform

It is likely to yield seismic data with missing traces in field data acquisition, however, the subsequent processing requires complete seismic data; therefore, it is more necessary to reconstruct missing traces in seismic data. The reconstruction of seismic data becomes a sparse optimization problem based on sparsity of seismic data in curvelet transform domain and the gradient projection algorithm is employed to solve it. To overcome the limitations of uncontrolled random sampling, the local random sampling is presented in the paper; it can not only control the size of the sampling gaps effectively, but also keep the randomness of the sampling. The numerical modeling shows that the reconstructed result of local random sampling is better than that of traditional random sampling and jitter sampling. In addition, the proposed approach is also applied to pre-stack shot gather and stacked section, the field examples indicate that this method is effective and applicable for the reconstruction of seismic data with missing traces again. Furthermore, this approach can provide satisfactory result for the following processing on seismic data. (C) 2015 Elsevier B.V. All rights reserved.