Using surface-related multiples in imaging for data with large acquisition gaps

Often seismic surveys are restricted due to unavoidable circumstances such as presence of platforms, busy sea routes and bad traces. These lead to missing data, which affects the migrated image severely. Since re-shooting the data to fill in these gaps is extremely expensive, migration strategies utilising the available data need to be developed in order to overcome this problem. Surface-related multiples illuminate a wider area, as they travel different propagation paths compared to primaries, becoming indispensable in such cases of limited illumination. However, existing migration methods that include surface-related multiples for imaging do so by re-injecting the total data as a downgoing wavefield; this makes the method dependent on dense receiver configuration and, therefore, inoperative in case of missing data. In this paper we demonstrate a ‘non-linear’ inversion approach on real data to overcome the effects of incomplete data. The results indicate substantial mitigation of effects on the image due to incomplete data and better images compared to the ‘linear’ inversion methods. Since in this method all the surface multiples are incrementally built from the original source field, it becomes dependent on the accuracy of the forward physical model and on the knowledge of the source wavelet. Therefore, we finally demonstrate that a hybrid approach combining both the ‘linear’ and ‘non-linear’ approaches provides the best results.