Blended Source Imaging by Amplitude Encoding

The computational cost of conventional imaging is large for today’s wide-azimuth seismic surveys. One strategy to reduce the overall cost of seismic imaging is to migrate with multiple shot-gathers at once, a technique which is known as blended source imaging. Blended source imaging trades the reduced cost of imaging with the presence of artifacts (cross-talk) in the image. We show that a theoretical framework using a matrix representation of the imaging process adequately describes conventional, and blended source imaging. Furthermore, the matrix representation predicts both the quantity and strength of cross-talk artifacts prior to imaging, thus allowing us to decide a priori the trade off between cross-talk and speed. By exploiting our theoretical framework, we are able to design an amplitude encoding scheme, referred to as Truncated Singular Vector (TSV), that trades a significantly reduced cost of imaging with spatial resolution and cross-talk noise. The TSV encoding allows us to reduce the cost of imaging by at least an order of magnitude relative to conventional shot-record migration. Overall, we provide a framework for finding blended source encoding schemes, that produce good quality images at lower computational cost.