Using surface multiples to image across large acquisition gaps

While surface-related multiples have long been considered as noise in seismic data, recently the consensus is changing to accept multiples as a tool for better illumination of the subsurface. In this paper we explore the different strategies of exploiting the surface-related multiples specifically in case of large acquisition gaps. Since surfacerelated multiples travel different propagation paths compared to the primaries, they illuminate a wider area making them vital in such cases of limited illumination. Existing imaging methods include surface-related multiples by reinjecting the total measured data as a downgoing wavefield. This however, makes the method dependent on a dense receiver configuration and, therefore, sensitive to missing data. Addressing a way around this problem, we will illustrate a `non-linear' inversion approach in which all multiples are modeled from the original source field. Such modeling makes the method less dependent on the receiver geometry, therefore, helping us in case of limited illumination. We also demonstrate a `hybrid' method on both synthetic and field data to overcome the effects of incomplete data by utilising the benefits of both `linear' and `non-linear' method. The results indicate substantial mitigation of effects on the image previously caused due to incomplete data and better images compared to the `linear' inversion methods.