State of the art methods to image the Earth's subsurface using active-source seismic reflection data involve reverse-time migration (RTM). This, and other standard seismic processing methods such as velocity analysis, provide best results only when all waves in the data set are primaries (waves reflected only once). A variety of methods are therefore deployed as pre-processing to predict multiples (waves reflected several times); however, accurate removal of those predicted multiples from recorded data using adaptive subtraction techniques proves challenging, even in cases where they can be predicted with reasonable accuracy. We describe a new, alternative strategy: we construct a parallel data set consisting of only primaries, which is calculated directly from recorded data. This obviates the need for both multiple prediction and removal methods. Primaries are constructed using convolutional interferometry to combine first arriving events of up-going and direct-wave down-going Green's functions to virtual receivers in the subsurface. The required up-going wavefields to virtual receivers are constructed by Marchenko redatuming. Crucially, this is possible without detailed models of the Earth's subsurface velocity structure. The method is shown both to be particularly robust against errors in the reference velocity model used, and to improve migrated images substantially. @en

Marchenko redatuming retrieves Green’s functions inside an unknown medium, by solving a set of coupled Marchenko equations, which are derived from an under-determined system of equation and two temporal truncations. To constrain the problem, two assumptions are made, which hold reasonably well for acoustic, but not for elastodynamic waves. First, an early part of the inverse transmission field is needed which can be estimated for sufficiently-simple acoustic cases, but remains hard to predict for elastic media without detailed overburden knowledge. Secondly, the scheme assumes temporal separability of up-going focusing and Green’s functions, which holds for many acoustic media but easily fails in presence of elastic effects. The impact of the failure to meet these assumptions is a somewhat controllable problem in 1.5D media. Independently, in acoustic media one can use a time-only focusing to retrieve focusing functions which collapse to a single plane wave below the overburden. We apply this approach to elastic data from a very complex almost 1.5D medium. The numerical example shows that the plane-wave approach can also be combined with mitigation of failure to satisfy the aforementioned assumptions and the result could lead to a high-fidelity internal multiple-free image.@en