Preconditioning seismic data with 5D interpolation for computing geometric attributes

Abstract

The most common preconditioning of seismic data improves the signal-to-noise (S/N) ratio of the seismic data by removing spatial noise or enhancing the coherency and alignment of the reflection events, without unnecessary smoothing or smearing of the discontinuities. Although we usually think of removing unwanted features, we can also improve the S/N by predicting unmeasured signal, such as dead traces and lower-fold areas corresponding to unrecorded offsets and azimuths in the gathers. Missing offsets and azimuths almost always negatively impact prestack inversion and AVAz analysis. While missing offsets and azimuths may not result in sufficiently reduced S/N of stacked data to impair conventional time-structure interpretation, they usually give rise to attribute artifacts. If the inconsistencies in fold follow a regular pattern, we refer to the corresponding attribute pattern as “acquisition footprint.” Acquisition footprint is an undesirable artifact that masks the geologic features or amplitude variations seen on time slices from the seismic data, especially at shallow times. We begin our article by correlating missing data and areas of low fold to artifacts seen in seismic attributes. We then show how 5D interpolation of missing data prior to prestack migration results in more complete gathers resulting in a better balanced stack and the reduction of footprint and other attribute artifacts.