High-Resolution Stacking of Seismic Data With Fast Capon Beamforming

Stacking plays an essential role in improving the quality of seismic imaging. Conventional stacking is performed by simply averaging the traces within a common-midpoint (CMP) gather after normal-moveout (NMO) correction or a common image gather (CIG) after prestack migration, leading to a stacked trace with limited resolution when seismic signals are not perfectly aligned. This letter presents a modified Capon beamforming algorithm, called Capon stacking, to improve the vertical (temporal or depth) resolution of seismic stacked images. The Capon stacking approach applies a data-dependent weight function to the prestack gather in the lateral direction to filter out misaligned distortions and irregularities in the signal before stacking. The weight function is computed separately for each frequency component using statistical analysis of the NMO-corrected CMP gather or migrated CIG. The implementation of the Capon beamforming method typically involves calculating the inverse of a relatively large-scale covariance matrix, which can be computationally intensive compared to conventional stacking. We propose a fast implementation of the Capon beamforming that yields the same result as the conventional approach, but without performing matrix inversion. Our numerical tests demonstrate that the proposed Capon stacking method is effective and efficient in improving the vertical resolution of seismic images.