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Abstract

Summary

We describe an inversion-based approach to de-ghosting of multicomponent data which uses hybrid operators to combine all available components without ghost model when the signal-to-noise ratio on the acceleration measurements is high and switching to single component input and an operator with ghost model when this ratio is low. The approach is found to be flexible in handling accelerometer noise that is localized in either frequency or space, or both. The problem is formulated in 3D frequency-wavenumber-space domain with anti-aliasing guided by low-frequency data. Any variations in depth of the streamer are explicitly handled and while use of ghost models is kept to a minimum, there is in any case no need to rely on the assumption of a flat sea-surface. Supporting real data examples from the Barents Sea are shown to demonstrate the results of successful wavefield separation, even in the presence of substantial residual noise on the input measurements.

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/content/papers/10.3997/2214-4609.202210526
2022-06-06
2024-04-19
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References

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