An iterative curvelet thresholding algorithm for seismic random noise attenuation

作者：Wang De-Li^*; Tong Zhong-Fei; Tang Chen; Zhu Heng

来源：Applied Geophysics, 2010, 7(4): 315-324.

DOI：10.1007/s11770-010-0259-8

• curvelet transform
• iterative thresholding
• random noise attenuation

In this paper, we explore the use of iterative curvelet thresholding for seismic random noise attenuation. knew method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm. Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm, FX deconvolution, and wavelet thresholding, the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio (SNR) and give higher signal fidelity at the same time. Furthermore, to make better use of the curvelet transform such as multiple scales and multiple directions, we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.

• 出版日期2010-12
• 单位吉林大学