Weighted stacking of seismic AVO data using hybrid AB semblance and local similarity

Common-midpoint (CMP) stacking technique plays an important role in enhancing the signal-to-noise ratio (SNR) in seismic data processing and imaging. Weighted stacking is often used to improve the performance of conventional equalweight stacking in further attenuating random noise and handling the amplitude variations in real seismic data. In this study, we propose to use a hybrid framework of combining AB semblance and local-similarity-weighted stacking scheme. The objective is to achieve an optimal stacking of the CMP gathers with class II amplitude-variation-with-offset (AVO) polarity-reversal anomaly. The selection of high-quality near-offset reference trace is another innovation of this work because of its better preservation of useful energy. Applications to synthetic and field seismic data demonstrate a great improvement using our method to capture the true locations of weak reflections, distinguish thin-bed tuning artifacts, and effectively attenuate random noise.