Interferometric imaging condition for wave-equation migration

The fidelity of depth seismic imaging depends on the accuracy of the velocity models used for wavefield reconstruction. Models can be decomposed in two components corresponding to large scale and small scale variations. In practice, the large scale velocity model component can be estimated with high accuracy using repeated migration/tomography cycles, but the small scale component cannot. Therefore, wavefield reconstruction does not completely describe the recorded data and migrated images are perturbed by artifacts. There are two possible ways to address this problem: improve wavefield reconstruction by estimating more accurate velocity models and image using conventional techniques (e.g. wavefield crosscorrelation), or reconstruct wavefields with conventional methods using the known smooth velocity model, and improve the imaging condition to alleviate the artifacts caused by the imprecise reconstruction, as suggested in this paper. In this paper, the unknown component of the velocity model is described as a random function with local spatial correlations. Imaging data perturbed by such random variations is characterized by statistical instability, i.e. various wavefield components image at wrong locations that depend on the actual realization of the random model. Statistical stability can be achieved by local wavefield averaging either in spatial windows defined in the vicinity of the data acquisition locations, or in local windows defined in the vicinity of image points. We use the latter approach and show that the technique is effective in attenuating imaging artifacts without being hampered by some of the limitations of data-space alternatives.