1887

Abstract

Waveform inversion estimates a quantitative model of the subsurface by minimizing the differences (residuals) between observed and calculated seismic data. The success of waveform inversion depends on the complexity of the misfit function. If the starting model is not in the neighbourhood of the global minimum, it can cause the inversion to fail and converge into a local minimum (Sirgue et al., 2011). Since low-frequency data are more linear with respect to the model misfit than high-frequency data, most waveform inversion implementations adopt a strategy that proceeds sequentially from low to high frequencies. Therefore, data preconditioning for waveform inversion must preserve as much low frequency signal as possible. Traditionally, the bubble pulse generated by the source in marine acquisitions has been removed from the data. The bubble can generate undesired results in, e.g., data-driven multiple prediction algorithms such as SRME (Verschuur et al., 1991), where the auto-convolution of the bubble can generate long period artefacts, and requires long filters in adaptive subtraction step. It is difficult to constrain the adaptive subtraction to preserve the primaries untouched when a long filter is used. However, it has also been recognized that the bubble pulse contains valuable low-frequency signal that can benefit the quality of the velocity model estimated by waveform inversion. We propose a workflow for waveform inversion data preconditioning that preserves the bubble and low frequency signal while effectively attenuating the free surface multiples.

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/content/papers/10.3997/2214-4609.20149824
2012-07-04
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20149824
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