Abstract: Data reconstruction is a critical preliminary work in the processing of seismic data.Compressed sensing (CS) has been well applied in data reconstruction.The key to CS is random sampling,which converts the mutual coherent alias caused by regular under-sampling into lower-amplitude incoherent noise. But traditional sampling methods lack constraints on sampling points, resulting in excessive noise interference. The segmented random sampling (SRS) method can effectively control the distance between sampling points. Furthermore, a single mathematical transformation will lead to incomplete sparse representation and impact data reconstruction. The morphological component analysis (MCA) can decompose a signal into several components with outstanding morphological features to approximate the complex internal structure of data. A new dictionary combination (Shearlet+DCT) has been found under the MCA framework, and the block coordinate relaxation (BCR) algorithm has been used to get the optimal solution to obtain desired reconstruction results. Tests of real data have proven that the proposed method can produce good effects when used to reconstruct the SRS data.