Imaging the Near Surface Using Surface-consistent Prediction Operators - Examples from the Middle East

The scope of this paper is to illustrate how surface-consistent deconvolution operators can help to image the shallow subsurface on land data. Two case studies from broadband, dense, wide-azimuth surveys recently acquired in Oman are presented. The predictive deconvolution operators were computed from an advanced simultaneous inversion of surface-consistent scalars and autocorrelations. Source and receiver operator volumes are compared to the migrated stack of primary reflections. A good match is observed, meaning that surface multiples were captured by the prediction operators. Furthermore, a significant improvement in the imaging of the shallow layers is achieved up to very shallow times. Some structures that are almost invisible on the migrated stack are revealed and the shallow reflectivity is recovered in undershoot areas. A good correlation with a shallow velocity well log is also observed. The deconvolution operators are derived from high fold, good quality reflection data. Therefore, they overcome the usual difficulties of near surface imaging from primaries such as low, irregular near-offset coverage and strong noise contamination. These high-resolution reflectivity volumes can be used as a guide for velocity model building of the shallow subsurface or as an input to internal or surface multiple modelling.