The classical least-squares migration is implemented based on a waveform misfit function in the data domain to accom-plish quantitative inversion of subsurface reflectivity.However,it is hard to achieve remarkable results due to unknown wavelet,in-accurate migration velocity,and unacceptable computational cost.To address these issues,we provide a practical least-squares re-verse-time migration method in the image domain.This method adopts a background velocity field and a given wavelet with real frequency band to compute the globally spatial-varying point-spread function,and uses an image-domain high-dimensional spatial deconvolution algorithm for high-resolution imaging.This method sidesteps seismic waveform misfit,and focuses on the illumina-tion of the point-spread function at different wavenumbers.As a result,the accuracy of the seismic wavelet and migration velocity does not make a strong impact on imaging.Moreover,one-iteration imaging with high computational efficiency facilitates its appli-cation to 3D seismic exploration in ultra-deep zones.Two case studies dealing with different reservoir types in northwestern China demonstrate that this method can improve imaging resolution for the description of fracture-cave structures and thus offer techni-cal support to reserves and production increase in ultra-deep carbonate reservoirs.
least-squares migrationimage domainpoint-spread functionreverse-time migrationultra-deep zone in northwestern China
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