Modern seismic acquisition is trending toward recording high-channel count data with smaller field arrays or single sensors. Reducing the size of field arrays leads to a deterioration of data quality. Many processing steps requiring estimation of prestack parameters become more challenging due to the low signal-to-noise ratio (SNR) of the data. Conventional processing algorithms require estimation of velocities, statics, and surface-consistent scalars and deconvolution operators, and need good prestack data quality. This 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 utilization of neighboring traces. The first method, called supergrouping, performs local summation of traces using a global normal moveout correction to align reflected signals. The second approach, called nonlinear beamforming (NLBF), is a data-driven procedure for estimating 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. We show that application of supergrouping and nonlinear beam forming (NLBF) provides significant uplift for various steps of land seismic processing such as deconvolution, estimation of statics, first-break picking, full-waveform inversion (FWI), and imaging.

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