Gleaning Meaningful Information from Seismic Attributes

Poststack seismic data loaded on workstations for interpretation exhibit a certain level of noise. This noise could be of various sorts - acquisition related, processing artifacts or random. In this presentation we focus on conditioning our seismic data prior to attribute computation. We discuss the use of some of the procedural steps for noise filtering and dip-steering options for computation of some geometric attributes like coherence and curvature. In addition, we discuss the impact the choice of algorithm can have on the final results. All these factors ensure that the seismic attributes yield more accurate information for interpretation.