Outliers-out stack: a new algorithm for processing seismic data

Abstract

Common midpoint (CMP) stacking is one of the essential stages in seismic data processing. Conventional straight mean stacking is based on the assumption that all signals are coherent while all noises are random, which is not always valid in practice. Consequently, many alternative stacking techniques have been introduced in the field of seismic data processing during the past five decades. In this study, a new alternative stacking procedure, called outliers-out (OlO) stack, is proposed and tested using both synthetic and field seismic data. The OlO stacking method is based on analysing the statistical spread of each time sample and excluding a distinctive range of outliers from each time sample. Outliers are calculated independently for each time sample based on the amplitudes distribution within the sample. The outliers’ exclusion process is automatic but it is also adjustable by the user to suite the data under processing. The experimental results of applying the proposed stacking method to both synthetic and field seismic data clearly show that the OlO stacking technique outperforms conventional stacking techniques in terms of the temporal resolution and lateral coherency of the output seismic reflections. Results also show that the tuning function in the OlO stacking algorithm is efficient in dealing with different types of seismic data.