LAUSR

Kirchhoff beam migration (KBM) is a ray-based seismic imaging method, which can handle multi-arrivals caused by model complexity. Apart from its high imaging precision, it also retains the merits of Kirchhoff migration, such as efficiency, stability, and flexibility. However, two issues should be taken into consideration when this method is expanded to the complicated surface conditions: first, the computational accuracy deficiency of the original local plane-wave decomposition method cannot suit for low signal-to-noise ratio seismic data; second, as the rays traveling, the beam width increases rapidly, which cannot guarantee the computational accuracy of the corresponding grid points’ attribute information. In addition, the insufficient coverage of the beam in the shallow part of the model might affect the imaging quality of this region. Kirchhoff dynamic focused beam migration based on compressed sensing is proposed to resolve these two problems. For the first problem, the local plane-wave decomposition method based on compressed sensing is introduced into KBM to enhance its computational accuracy. To solve the second problem, this paper adopts the dynamic focused beam to replace the original simplified Gaussian beam in the migration method, control the divergence of beam, and increase the coverage of beam in the shallow part of the model. Both Marmousi model and Canadian Foothills model are employed in this paper to test the new migration imaging method.