Abstract: Conventional migration imaging,which only considers primary reflected waves,fails to effectively image subsurface high-steep geobodies.Compared to primary reflected waves,prismatic waves travel an additional path, enabling the imaging of high-steep structures.However,the additional travel path increases the computational load.Seismic interferometry can shift the surface observation system downward to an artificially selected subsurface calibration plane.Consequently,subsequent calculations only need to be performed on the model below the subsurface calibration plane,there by improving computational efficiency.Hence,this study proposed a novel imaging method integrating prismatic waves and seismic interferometry.In the proposed method,the acoustic wave equation was replaced by the prismatic wave equation.Virtual records were generated through cross-correlations between data from the surface and subsurface reference planes.These records were combined with reverse time migration(RTM) for imaging.The proposed method was verified using the horizontal layered, L-shaped, and salt dome models.Specifically,the horizontal layered model exhibited consistent imaging results with those below the global reference plane;the L-shaped model outperformed conventional methods in imaging steep structures;the salt dome model displayed enhanced pre-salt imaging resolution.Due to the downward shift of the surface observation system,the following imaging process entailed a reduced vertical depth and a shorter shot record time.The proposed method reduced the computational cost to 26.6% of that using the conventional RTM imaging.Overall,through the downward shift of the observation system and the utilization of multiples,the proposed method achieved both satisfactory accuracy and efficiency,overcoming the limitations of traditional interferometry and providing a novel solution for exploring high-steep structures.Notably,there still exist discrepancies between the simplified models and the actual heterogeneity,requiring further optimization of the computational cost.