摘要
Ocean-Bottom Node(OBN)acquisitions provide both non-converted and converted reflection energy.There is a clear advantage to independently imaging both P-and S-waves,as they provide more information collectively than either does alone.In many conventional converted-wave pre-stack migration algorithms,density is treated as a constant,which is not the real-life case on earth.S-wave velocity and density information is crucial for hydrocarbon detection because it helps in the identification of porefilling fluids.In this paper,we focused on the effect of density on imaging,and developed a method of reverse-time migration(RTM)on converted s-waves of varying densities(VD-RTMCS).Phase correction was required prior to pre-stack migration to avoid constructive interference between data from adjacent sources.Synthetic data examples showed that when density variations were included,image profiles showed advantages in signal-to-noise ratio,vertical resolution and imaging of complex structures.
Ocean-Bottom Node(OBN) acquisitions provide both non-converted and converted reflection energy. There is a clear advantage to independently imaging both P-and S-waves, as they provide more information collectively than either does alone. In many conventional converted-wave pre-stack migration algorithms, density is treated as a constant, which is not the real-life case on earth. S-wave velocity and density information is crucial for hydrocarbon detection because it helps in the identification of porefilling fluids. In this paper, we focused on the effect of density on imaging, and developed a method of reverse-time migration(RTM) on converted s-waves of varying densities(VD-RTMCS). Phase correction was required prior to pre-stack migration to avoid constructive interference between data from adjacent sources. Synthetic data examples showed that when density variations were included, image profiles showed advantages in signal-to-noise ratio, vertical resolution and imaging of complex structures.
基金
supported by the National Science and Technology Major Project (No. 2016ZX05027-002)
the National Natural Science Foundation of China (No. 41230 318)
Qingdao National Laboratory for Marine Science and Technology Innovation Project of Ao-Shan (No. 2015ASKJ03)
