Application of supergrouping to enhance 3D prestack seismic data from a desert environment

Abstract

Fit-for-purpose enhancement remains a critical task for processing land seismic data, especially with the increasing popularity of high-channel-count and single-sensor data. We describe here a flexible scheme called smart supergrouping that performs summation of traces from neighboring shots and receivers. Supergrouping enhances desired reflection signals while suppressing ground roll and other noise. It also delivers prestack data of significantly high quality, critical for deriving velocities, deconvolution operators, scalars, and statics, as well as for improved imaging. While similar in concept to field source/receiver array forming, supergrouping may be applied to data already acquired with field arrays. Unlike field arrays, supergrouped data have kinematic corrections applied with overlapping apertures. We also have the ability to compensate for intra-array statics and wavelet variations. We demonstrate the signal-enhancing abilities of such generalized supergroups with normal-moveout corrections on challenging land and ocean-bottom-cable seismic data from Saudi Arabia. Applications of supergrouped data cover various steps of land seismic processing from first-break picking to deconvolution to statics to full-waveform inversion and imaging.