A Generalized Seismic Attenuation Compensation Operator Optimized by 2-D Mathematical Morphology Filtering

Improving resolution and signal-to-noise ratio (SNR) are common goals in seismic data processing, but the two are generally in mutual opposition. A more realistic key objective is to attain robustness. In this work, we propose a novel generalized attenuation compensation operator (GACO) for enhancing seismic resolution. The new operator is controlled by two parameters. It offers improved flexibility and adaptability compared with traditional operators, which can be considered as special cases. The 2-D mathematical morphology filtering (2-D MMF) optimized GACO is developed for attenuation compensation of low SNR seismic data with good noise immunity and robustness. An SNR coefficient based on the time-frequency domain 2-D MMF is proposed for detecting the high SNR regions of seismic records for attenuation compensation and noise suppression. Therefore, the compensated seismic data has higher resolution and higher SNR after applying the new approach. Experimental results show that the proposed scheme is practical, adaptive, and effective. More importantly, it offers good noise immunity and especially robustness in processing of strongly attenuated low SNR data.