Least-squares Migration of Multisource Data with a Deblurring Filter

Least-squares migration (LSM) has been shown to be able to produce high quality migration images, but its computational cost is considered to be too high for practical imaging. In this chapter, a multisource least-squares migration algorithm (MLSM) is proposed to increase the computational efficiency by utilizing the blended sources processing technique. To expedite convergence, a multisource deblurring filter is used as a preconditioner to reduce the data residual. This MLSM algorithm is applicable with Kirchhoff migration, wave-equation migration or reverse time migration, and the gain in computational efficiency depends on the choice of migration method. Numerical results with Kirchhoff least-squares migration on the 2D SEG/EAGE salt model show that an accurate image is obtained by migrating a supergather of 320 phase-encoded shots. When the encoding functions are the same for every iteration, the I/O cost of MLSM is reduced by 320 times. Empirical results show that the crosstalk noise introduced by blended sources is more effectively reduced when the encoding functions are changed at every iteration. The analysis of signal-to-noise ratio (SNR) suggests that not too many iterations are needed to enhance the SNR to an acceptable level. Therefore, when implemented with wave-equation migration or reverse time migration methods, the MLSM algorithm can be more efficient than the conventional migration method.