2-D and 3-D Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions and Excitation-Amplitude Imaging Condition

The enormous computational overheads and excessive storage requirements are two obstacles to the data-domain least-squares reverse time migration (RTM) approach for the application of large-scale 3-D seismic data. To alleviate this problem, we have developed an image-domain least-squares RTM (IDLSRTM) approach through point spread functions (PSFs) and excitation-amplitude (EA) imaging condition, denoted as EA-IDLSRTM. The key point is that the EA imaging condition, as a cost-effective and practical imaging condition, is used to reconstruct the RTM image and localized PSFs. There are two benefits to this combination. One is that the EA imaging condition can effectively reconstruct the RTM image and localized PSFs with less computational overhead and storage requirement, relative to the zero-lag cross correlation (CC) imaging condition. Another important benefit is that the redundant source wavelets in both the RTM and PSF images computed by the CC imaging condition can be removed by the EA imaging condition, prior to the image-domain inversion. As a result, the proposed approach can explicitly reduce the condition number of the Hessian matrix used in the conventional IDLSRTM approach, which will produce a less ill-conditioned inverse problem. In addition, we introduce an angle-dependent filter for the attenuation of low-wavenumber artifacts to accelerate the convergence. Several experiments with synthetic and field data demonstrate that the proposed EA-IDLSRTM approach can efficiently and effectively recover the high-resolution and high-fidelity reflectivity image. Meanwhile, EA-IDLSRTM can provide better imaging quality than the conventional IDLSRTM approach in the case of relatively smoothed velocity.