The Application Of Outliers-Out Stacking For Seismic Noise Reduction In Synthetic And Real Data - Agus Riyanto; Novrizal; Supriyanto; Abdul Haris (Jurnal Geosains Terapan, 2020)
Seismic data processing is an important stage to improve the signal-to-noise (S/N) ratio and the seismic resolution in the seismic method. The invention of new methods for seismic data processing needs to be examined for its effectiveness and its implications to the whole sequences in the seismic data processing. Outliers-Out Stack (OlO Stack) is a new alternative for stacking method. This method is claimed more effective in eliminating bad sample data, which is contaminated by the outliers. Further, this method is able to improving stacking methods to become optimum. In this study, we applied the OlO Stack method to the synthetic and real data set by comparing its result with 2 other stacking methods, i.e., Mean Stack and α-Trimmed Stack. The three methods are applied to 2D seismic marine data, which have been varied at some processing stages. The results show that the application of OlO Stack in seismic data processing is less effective to reduce seismic random noise compared to the two other methods. However, the application to the synthetic data set with the NMO case, the OlO stack shows better quality, which is indicated by a high correlation coefficient of 0.892196 and a lower RMS error of 0.109851. These results were confirmed by the real data set, where the OlO Stack data is able to reduce ringing and suppress multiple energy better than the other two methods.