Least-squares migration/inversion of blended data Yaxun Tang and Biondo Biondi ABSTRACT We present a method based on least-squares migration/inversion to directly im- age data collected from recently developed wide-azimuth acquisition geometries, such as simultaneous shooting and continuous shooting, where two or more shot records are often blended together. We show that by using least-squares mi- gration/inversion, we not only enhance the resolution of the image, but more importantly, we also suppress the crosstalk or acquisition footprint, without any pre-separation of the blended data. We demonstrate the concept and methodol- ogy in 2-D and apply the data-space inversion scheme to the Marmousi model, where an optimally reconstructed image, free from crosstalk artifacts, is obtained. INTRODUCTION High-quality seismic images are extremely important for subsalt exploration, but data collected from conventional narrow-azimuth towed streamers (NATS) often produce poor subsalt images due to insufficient azimuth coverage. Recently developed wide- azimuth towed streamers (WATS) (Michell et al., 2006) and multi-azimuth towed streamers (MATS) (Keggin et al., 2006; Howard and Moldoveanu, 2006) acquisition technologies have greatly improved subsalt illumination, and hence better subsalt images are obtained. However, acquiring marine WATS or MATS data is expensive. One main reason is the inefficiency of the conventional way of acquiring data, which requires waiting long enough between shots to prevent interference (Beasley et al., 1998; Beasley, 2008; Berkhout, 2008). As a consequence, the source domain is often poorly sampled to reduce the survey time. To gain efficiency, simultaneous shooting (Beasley et al., 1998; Beasley, 2008; Hampson et al., 2008) and continuous shooting, or more generally, blended acquisition geometry (Berkhout, 2008), have been proposed to replace the conventional shooting strategy. In the blended acquisition geometry, we try to keep shooting and recording continuously, so that waiting between shots is minimized and a denser source sampling can be obtained. However, this shooting and recording strategy results in two or more shot records blending together and brings processing challenges. A common practice for processing these blended data is to first separate the blended shot gathers into individual ones in the data domain (Spitz et al., 2008; Akerberg et al., 2008), called ”deblending” by Berkhout (2008). Then conventional processing flows are applied to SEP–138