TY - GEN
T1 - 3D multi-source least-squares reverse time migration
AU - Dai, Wei
AU - Boonyasiriwat, Chaiwoot
AU - Schuster, Gerard
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2010/1/1
Y1 - 2010/1/1
N2 - We present the theory and numerical results for least-squares reverse time migration (LSRTM) of phase-encoded supergathers, where each supergather is the superposition of phasedencoded shots. Three type of encoding functions are used in this study: random time shift, random source polarity and random source location selected from a pre-designed table. Numerical tests for the 3D SEG/EAGE Overthrust model show that multi-source LSRTM can suppress migration artifacts in the migration image and remove most of the crosstalk noise from multi-source data. Empirical results suggest that multisource LSRTM can provide a noticeable increase in computational efficiency compared to standard RTM, when the CSGs in a supergather are modeled and migrated together with a finite-difference simulator. If the phase-encoding functions are dynamically changed after each iteration of LSRTM, the best images are obtained. The potential drawback is that the final results are very sensitive to the accuracy of the starting model.
AB - We present the theory and numerical results for least-squares reverse time migration (LSRTM) of phase-encoded supergathers, where each supergather is the superposition of phasedencoded shots. Three type of encoding functions are used in this study: random time shift, random source polarity and random source location selected from a pre-designed table. Numerical tests for the 3D SEG/EAGE Overthrust model show that multi-source LSRTM can suppress migration artifacts in the migration image and remove most of the crosstalk noise from multi-source data. Empirical results suggest that multisource LSRTM can provide a noticeable increase in computational efficiency compared to standard RTM, when the CSGs in a supergather are modeled and migrated together with a finite-difference simulator. If the phase-encoding functions are dynamically changed after each iteration of LSRTM, the best images are obtained. The potential drawback is that the final results are very sensitive to the accuracy of the starting model.
UR - http://hdl.handle.net/10754/594720
UR - http://library.seg.org/doi/abs/10.1190/1.3513494
U2 - 10.1190/1.3513494
DO - 10.1190/1.3513494
M3 - Conference contribution
SN - 9781617389801
T3 - Society of Exploration Geophysicists International Exposition and 80th Annual Meeting 2010, SEG 2010
SP - 3120
EP - 3124
BT - Society of Exploration Geophysicists International Exposition and 80th Annual Meeting 2010, SEG 2010
PB - Society of Exploration Geophysicists
T2 - Society of Exploration Geophysicists International Exposition and 80th Annual Meeting 2010, SEG 2010
Y2 - 17 October 2010 through 22 October 2010
ER -