Chapter 2: multisource least-squares reverse time migration

The least-squares migration method (Duquet et al., 2000; Lailly, 1984; Schuster, 1993; Cole and Karrenbach, 1992; Nemeth et al., 1999) has been shown to sometimes produce migration images with better quality than those computed by conventional migration. When least-squares migration is implemented with the RTM method (Dai et al., 2010; Wong et al., 2011; Dai et al., 2012; Tang and Biondi, 2009; Dai and Schuster, 2010a), it can reduce not only the acquisition footprint but also the artifacts in the RTM image, while enhancing the image resolution.

In this chapter, I propose an efficient multisource least-squares reverse time migration algorithm for a VTI medium. With blended sources processing, many conventionally acquired shot gathers are phase-encoded and blended together to form supergathers to reduce the computational cost and I/O burden of migration (Romero et al., 2000; Dai and Schuster, 2009,2010b; Krebs et al., 2009). However, blended sources processing introduces crosstalk noise, which needs to be removed from the final migration images. Our synthetic results demonstrate that LSRTM can mitigate both migration artifacts and crosstalk noise introduced by phase encoding, balance the amplitudes of reflectors, and improve the spatial resolution of the image. Moreover, the efficiency of multisource LSRTM can be significantly higher than conventional RTM depending on the number of shots encoded in one supergather, the number of migration operations at every iteration, and the number of iterations needed for an image of acceptable quality.