Joint Reconstruction of Two‐Component Seismic Data Based on Compressed Sensing and Complex Curvelet Transform

Multicomponent seismic exploration technology comprehensively utilizes the dynamics and kinematics characteristics of seismic waves, which can reduce the non‐uniqueness of seismic imaging and predict hydrocarbon‐bearing reservoirs with high accuracy. However, multicomponent seismic data are often incomplete due to the constraints from field environment and acquisition costs. To address the problem of reconstructing multicomponent seismic data, conventional compressed sensing (CS)‐based algorithms reconstruct each component independently, failing to exploit implicit relationships between different components. By mapping different components to the real and imaginary parts of complex numbers, we can quantify their intrinsic correlations through the mathematical structure of complex numbers and preserve the cross‐information between different components. Therefore, under the framework of CS, we construct the objective function of joint reconstruction of two‐component seismic data and propose a joint reconstruction method of two‐component seismic data based on CS and complex Curvelet transform (CCT). First, we introduce complex numbers to establish the implicit relationship between different components by taking them as real and imaginary parts. The complex numbers constructed from different components are then subjected to the CCT as a whole. In this process, the joint sparsity of the two components is used as a priori information for data reconstruction. Finally, the proposed reconstruction model is solved using an improved fast projection onto convex sets. Experiments with synthetic and field data demonstrate that the proposed method effectively achieves the joint reconstruction of two‐component seismic data. Compared with reconstruction methods only using a single component of seismic data, the proposed method exhibits higher computational efficiency and accuracy without increasing data dimensions.