In an increasingly complex exploration environment, the acquired seismic data are incomplete and irregular, and field interference often seriously affects the S/N (signal-to-noise ratio) of complex seismic signals. At the same time, weak seismic signals in deep geophysical target are always covered by field noise. How to effectively reconstruct irregular seismic signals and suppress field noise is still a key problem in high-precision seismic exploration. The paper systematically analyzes the inverse problem of sparse optimization for noise suppression of field interferences in irregular seismic data under the framework of 3D curvelet transform, and proposes an amplitude-preserving reconstruction technique for 3D irregular data based on improved POCS algorithm with iterative soft threshold. On the basis of achieving highly sparse representation, the effective seismic signal is inversed iteratively through the change of S/N of adjacent traces to effectively improve the S/N of complex seismic data. At the same time, when solving the sparse optimization problem, the dividing point of noise and effective wave curvelet coefficient is obtained by constructing the ratio formula of curvelet coefficient, which lays the foundation for accurately suppressing noises and reconstructing weak seismic signals. The application of actual data shows that the method can suppress the noises in the complex seismic data, recover the missing data effectively, and improve the fidelity of the reconstructed data.

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