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Abstract

Summary

Shearlet transform is a new multiscale transform with the optimal directional character. It has unique ability to capture the geometry of multidimensional data. Shearlet transform can be effective used for random noise attenuation by properly shrinking coefficients in shearlet domain. In this paper, we apply this method to seismic random noise reduction. Furthermore, we make an improvement to this method in denoising and signal-preserving of seismic data with low SNR by combining anisotropic diffusion. The effectiveness of the proposed method is tested on synthetic and real seismic data. The experimental results demonstrate that the proposed method can eliminate random noise more effectively and preserve the reflected events better than the shearlet transform denoising method.

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/content/papers/10.3997/2214-4609.201413318
2015-06-01
2024-03-28
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References

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