Enhancing Challenging Prestack Data Using Local Summation Approaches

Abstract

For high-channel count and single-sensor seismic data many processing steps requiring estimation of prestack parameters become more challenging due to the low signal-to-noise ratio of the data. Conventional processing algorithms involve estimation of velocities, statics and surface consistent scalars and operators, and need good prestack data quality, which is rarely the case for land seismic data acquired in arid desert environments of Saudi Arabia with a complex near surface. We present two methods for prestack seismic signal enhancement based on mixing neighboring traces. The first method called supergrouping performs local summation of traces using a global normal moveout correction to align reflected signal. The second approach, called nonlinear beamforming (NLBF), is a data-driven procedure to estimate local moveout directly from the data. We demonstrate the signal enhancement ability of these procedures on synthetic and challenging land seismic data from Saudi Arabia.