We present a method based on least-squares reverse time migration with plane-wave encoding （P-LSRTM） for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding fun...We present a method based on least-squares reverse time migration with plane-wave encoding （P-LSRTM） for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding function and fill constant velocity to the area above rugged topography in the model so that P-LSRTM can be directly performed from rugged surface in the way same to shot domain reverse time migration. In order to improve efficiency and reduce I/O （input/output） cost, the dynamic en- coding strategy and hybrid encoding strategy are implemented. Numerical test on SEG rugged topography model show that P-LSRTM can suppress migration artifacts in the migration image, and compensate am- plitude in the middle-deep part efficiently. Without data correction, P-LSRTM can produce a satisfying image of near-surface if we could get an accurate near-surface velocity model. Moreover, the pre-stack P- LSRTM is more robust than conventional RTM in the presence of migration velocity errors.展开更多
基金jointly financial support of the National 973 Project of China(Nos.2014CB239006,2011CB202402)the National Natural Science Foundation of China(Nos.41104069,41274124)+1 种基金the Shandong Natural Science Foundation of China(No.ZR2011DQ016)the Fundamental Research Funds for the Central Universities of China(No.R1401005A)
文摘We present a method based on least-squares reverse time migration with plane-wave encoding （P-LSRTM） for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding function and fill constant velocity to the area above rugged topography in the model so that P-LSRTM can be directly performed from rugged surface in the way same to shot domain reverse time migration. In order to improve efficiency and reduce I/O （input/output） cost, the dynamic en- coding strategy and hybrid encoding strategy are implemented. Numerical test on SEG rugged topography model show that P-LSRTM can suppress migration artifacts in the migration image, and compensate am- plitude in the middle-deep part efficiently. Without data correction, P-LSRTM can produce a satisfying image of near-surface if we could get an accurate near-surface velocity model. Moreover, the pre-stack P- LSRTM is more robust than conventional RTM in the presence of migration velocity errors.
