Summary Seismic acquisition in an area can often get hindered by reasons such as complex topography, infrastructure (e.g. platforms) or lack of access due to legal and environmental reasons. Such areas with possibilities of large data gaps may deter exploration or monitoring, as the conventional imaging strategies would either provide bad seismic images or turn out to be very expensive. Surface-related multiples travel different paths compared to primaries, illuminating a wider subsurface area. This property makes the surface-related multiples particularly important in case of data with large gaps. In this paper we show different strategies of using surface-related multiples to get around the problem of imaging with a large data gap. Conventional least-squares imaging methods that incorporate surface-related multiples do so by re-injecting the measured wavefield, which makes it sensitive to missing data. Therefore, we present a ‘non-linear’ imaging method that models all the surface-related multiples in the unacquired section from the original source field. Eventually we also demonstrate a ‘hybrid’ method that combines the ‘non-linear’ imaging method with the conventional ‘linear’ multiple imaging method, which further improves our imaging result. We test the methods on synthetic as well as field data. Despite large acquisition gaps, our method gives promising results.

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