最小二乘偏移能够压制不规则地震数据偏移时产生的偏移假象,但存在计算量巨大、收敛速度缓慢等问题。为了快速地压制偏移噪音,发展了基于奇异值谱约束的叠前平面波最小二乘逆时偏移方法(Plane-wave prestack least-squares reverse time migration, PLSRTM)。一方面,通过奇异值谱分析(Singular spectrum analysis, SSA)方法对角度域共成像点道集(Take-Off Angle Domain Common-Image Gathers, TADCIGs)进行预处理,以压制偏移假象,同时引入随机奇异值分解减小SSA的计算量。另一方面,由于偏移速度不准确时叠加成像结果不能有效地压制偏移噪音,将相邻角度平面波偏移结果的差异作为正则化约束加入误差泛函,以改善这一问题。对Marmousi模型数据的成像测试结果表明该方法能够快速压制平面波道集及不规则地震数据偏移产生的偏移假象,改善PLSRTM的成像质量;当偏移速度不准确时,该方法能够得到偏移噪音更少、构造更加连续的成像结果。
Least squares migration can eliminate the artifacts introduced by the direct imaging of irregular seismic data but is computationally costly and of slow convergence. In order to suppress the migration noise, we propose the preconditioned prestack plane-wave least squares reverse time migration (PLSRTM) method with singular spectrum constraint. Singular spectrum analysis (SSA) is used in the preconditioning of the take-off angle-domain common-image gathers (TADCIGs). In addition, we adopt randomized singular value decomposition (RSVD) to calculate the singular values. RSVD reduces the computational cost of SSA by replacing the singular value decomposition (SVD) of one large matrix with the SVD of two small matrices. We incorporate a regularization term into the preconditioned PLSRTM method that penalizes misfits between the migration images from the plane waves with adjacent angles to reduce the migration noise because the stacking of the migration results cannot effectively suppress the migration noise when the migration velocity contains errors. The regularization imposes smoothness constraints on the TADCIGs that favor differential semblance optimization constraints. Numerical analysis of synthetic data using the Marmousi model suggests that the proposed method can efficiently suppress the artifacts introduced by plane-wave gathers or irregular seismic data and improve the imaging quality of PLSRTM. Furthermore, it produces better images with less noise and more continuous structures even for inaccurate migration velocities.