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Sub-basalt imaging for hydrocarbon exploration faces challenges with the presence of multiple scattering, attenuation and mode-conversion as seismic waves encounter highly heterogeneous and rugose basalt layers. A combination of modern seismic acquisition that can record densely-sampled data, and advanced imaging techniques make imaging through basalt feasible. Yet, the internal multiples, if not properly handled during seismic processing, can be mapped to reservoir layers by conventional imaging methods, misguiding geological interpretation. Traditional internal multiple elimination methods suffer from the requirement of picking horizons of multiple generators and/or a top-down adaptive subtraction process. Marchenko imaging provides an alternative solution to directly remove the artifacts due to internal multiples, without the need of horizon picking or subtraction. In this paper, we present a successful application of direct Marchenko imaging for sub-basalt de-multiple and imaging with an offshore Brazil field dataset. The internal multiples in this example are generated from the seabed and basalt layers, causing severe artifacts in conventional seismic images. We demonstrate that these artifacts are largely suppressed with Marchenko imaging and propose a general work flow for data pre-processing and regularization of marine streamer datasets. We show that horizontally propagating waves can also be reconstructed by the Marchenko method at far offsets.

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The application of [Formula: see text] compensation to prestack marine data needs the proper removal of the water-layer time from the total traveltime, a process known as “time referencing.” To obtain the water-layer time, current industry practices use some form of normal moveout equation that requires subsurface velocities. We have derived a more straightforward and accurate formula for time referencing that does not require subsurface velocities and works under the same assumptions. The formula is based on a local angle decomposition via the tau-[Formula: see text] transform. Further complicating the [Formula: see text] compensation task in the prestack domain is the proper treatment of spatially aliased energy and high-frequency noise. We found out how time-slowness sparsity, used as a constraint for [Formula: see text] compensation, gives excellent immunity to incoherent noise and spatial aliasing, and we evaluate its role in accelerating the convergence rate for our iterative inversion algorithm.