The Marchenko method can reconstruct the Green’s function at any subsurface position by utilizing the surface reflection response and the background velocity model, which can be applied to structural target-oriented imaging. Compared with traditional seismic interferometry, the imaging is not affected by the overburden medium. However, the imaging quality of the Marchenko method relies on high-density acquisition, which field data sets often fail to provide. To address this issue, this paper proposes the Marchenko imaging method based on convolutional neural network projection onto convex sets (CNN-POCS) reconstruction. First, sparsely acquired seismic data are reconstructed using the CNN-POCS method to generate high-precision data with dense spatial sampling. Marchenko imaging is then applied to the target zone using the reconstructed data. This approach not only reduces the high-density acquisition requirement of the Marchenko method but also preserves imaging accuracy. Finally, the method is extended to plane-wave imaging, which improves the computational efficiency of the conventional point-source approach and reduces the computational cost. The model test results show that the proposed method can reduce the receiver deployment by 60%, thus significantly lowering acquisition costs.
Xu et al. (Sun,) studied this question.