Attribute-assisted footprint suppression using a 2D continuous wavelet transform

Abstract

Acquisition footprint manifests itself on 3D seismic data as a repetitive pattern of noise, anomalously high amplitudes, or structural shifts on time or horizon slices that is correlated to the location of the sources and receivers on the earth’s surface. Ideally, footprint suppression should be handled by denser seismic acquisition and more careful prestack processing prior to seismic imaging. In the case in which only legacy data exist, or when economic and time constraints preclude more expensive acquisition and more careful processing, interpreters must deal with data contaminated by footprint. Although accurate time-structure maps can be constructed from footprint-contaminated data, the effect of footprint on subsequent attributes, such as coherence, curvature, spectral components, and P-wave impedance will be exacerbated. We have developed a workflow that uses a 2D continuous wavelet transform to suppress coherent and incoherent noise on poststack seismic data. The method involves decomposing time slices of amplitude and attribute data into voices and magnitudes using 2D wavelets. We exploit the increased seismic attribute sensitivity to the acquisition footprint to design a mask to suppress the footprint on the original amplitude data. The workflow is easy to apply and improves the interpretability of the data and improves the subsequent attribute resolution.