Abstract
Summary The dip-dependency of the wavelet on migrated images can cause significant inaccuracies in the inverted impedances obtained from conventional inversion approaches based on 1D vertical convolutional modelling. Although the inaccuracies are dominant on the steeply dipping events, low-dip events may also suffer if they are contaminated with cross-cutting steep migration artefacts and smiles. An efficient, effective and reversible data pre-conditioning approach is proposed that corrects for dip-dependency of the wavelet and is applied to migrated images prior to inversion. The method consists of integrating with respect to the total wavenumber followed by differentiation with respect to the vertical wavenumber. This process is equivalent to applying a deterministic dip-consistent correction that projects the data from the total wavenumber to the vertical wavenumber axis. The proposed vertical image projection methodology reduces the impact of migration artefacts and improves inverted impedances in both synthetic and real data examples.
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