1887

Abstract

Summary

Changes in water velocity produce significant 4D noise in time-lapse images. To be addressed accurately, the water-velocity problem requires two major ingredients: 1) water velocity must be estimated accurately at all acquisition times and for all shot/receiver locations, 2) time-variable corrections to the data must be dynamic to treat the full wavefield accurately. We present a new approach to parameterization of water-velocity changes which minimizes sensitivity to water depth, allowing data redundancy to be exploited to increase robustness and precision of water velocity estimation. Dynamic correction of the wavefield is then achieved by designing 3D time-variable phase-shift operators that extrapolate data through the water column with a time-variable water velocity and re-extrapolate back to the acquisition datum with a stationary (reference) velocity. This is applied in a tau-px-py least-squares modeling process. Application of the method to deep-water OBN data shows significant improvements in data repeatability, decreasing 4D noise and increasing focus and clarity of the 4D signal.

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/content/papers/10.3997/2214-4609.201600587
2016-05-30
2024-03-28
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