Q least-squares reverse time migration with viscoacoustic deblurring filters
KAUST DepartmentEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2017-09-19
Print Publication Date2017-11
Permanent link to this recordhttp://hdl.handle.net/10754/625299
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AbstractViscoacoustic least-squares reverse time migration, also denoted as Q-LSRTM, linearly inverts for the subsurface reflectivity model from lossy data. Compared to the conventional migration methods, it can compensate for the amplitude loss in the migrated images due to strong subsurface attenuation and can produce reflectors that are accurately positioned in depth. However, the adjoint Q propagators used for backward propagating the residual data are also attenuative. Thus, the inverted images from Q-LSRTM are often observed to have lower resolution when compared to the benchmark acoustic LSRTM images from acoustic data. To increase the resolution and accelerate the convergence of Q-LSRTM, we propose using viscoacoustic deblurring filters as a preconditioner for Q-LSRTM. These filters can be estimated by matching a simulated migration image to its reference reflectivity model. Numerical tests on synthetic and field data demonstrate that Q-LSRTM combined with viscoacoustic deblurring filters can produce images with higher resolution and more balanced amplitudes than images from acoustic reverse time migration (RTM), acoustic LSRTM and Q-LSRTM when there is strong attenuation in the background medium. The proposed preconditioning method is also shown to improve the convergence rate of Q-LSRTM by more than 30 percent in some cases and significantly compensate for the lossy artifacts in RTM images.
CitationChen, Y., Dutta, G., Schuster, G., & Dai, W. (2017). Q least-squares reverse time migration with viscoacoustic deblurring filters. SEG Technical Program Expanded Abstracts 2017. doi:10.1190/segam2017-17640582.1
SponsorsThe research reported in this paper was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We are grateful to the sponsors of the Center for Subsurface Imaging and Modeling (CSIM) Consortium for their financial support. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST. We thank them for providing the computational resources required for carrying out this work. We also thank Schlumberger and BP for providing the BP2004Q dataset and Exxon for the Friendswood crosswell data.
PublisherSociety of Exploration Geophysicists