1887

Abstract

Summary

The curvelet transform is a known tool used in the attenuation of coherent and incoherent noise in seismic data. It utilises the fact that signal and noise are usually better separated in the curvelet domain than in the time-space (TX) domain. Coefficients of the transform are not independent and neighbouring coefficients are strongly correlated, which existing curvelet-based noise attenuation algorithms do not fully utilise. In this work we propose to use a data structure called a ‘dip map’ to describe dip information in seismic data. This information links local curvelet coefficients together in adaptive thresholding or subtraction of curvelet coefficients in seismic denoising algorithms. We used the dip map to improve curvelet multiple subtraction algorithm and the results show significant improvement over traditional methods with real data.

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/content/papers/10.3997/2214-4609.201701242
2017-06-12
2024-03-28
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References

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