Internal Multiple Prediction - A New Approach Based on Seismic Interferometry and Marchenko Autofocusing

Abstract

Standard seismic processing steps such as velocity analysis and reverse time migration (imaging) usually assume that all reflections are primaries: multiples represent a source of coherent noise and must be suppressed to avoid imaging artefacts. Suppressions methods are relatively ineffective for internal multiples. We show how to predict and remove internal multiples using Marchenko autofocusing and seismic interferometry. We first show how internal multiples can theoretically be reconstructed in convolutional interferometry by combining purely reflected, up- and down- going Green’s functions from virtual sources in the subsurface. We then generate the relevant up- and down-going wavefields at virtual sources along discrete subsurface boundaries using autofocusing. Then, we convolve purely scattered components of up- and down-going Green’s functions to reconstruct only the internal multiple field which is adaptively subtracted from the measured data. Crucially, this is all possible without detailed modelled information about the Earth’s subsurface. The method only requires surface reflection data and estimates of direct (non-reflected) arrivals between subsurface sources and the acquisition surface. The method is demonstrated on a stratified synclinal model and is particularly robust against errors in the velocity model used.