Abstract
In this paper, we explore the use of iterative curvelet thresholding for seismic random noise attenuation. A new 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.
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This work is supported financially by the National Science & Technology Major Projects (Grant No. 2008ZX05023-005-013).
Deli Wang, Professor at Jilin University. He received a BS (1995) and an MS (1998) in Applied Geophysics from Changchun College of Geology and a PhD (2002) in Geophysics from the College of Geoexploration Science and Technology, Jilin University. From 2006 to 2007, Wang was a visiting associate professor at the Seismic Laboratory for Imaging and Modeling, Department of Earth and Ocean Sciences, University of British Columbia, Canada. His research interests include high-resolution seismic data processing, seismic modeling, and parameter inversion in anisotropic media. Now his research focuses on primary-multiple separation, seismic denoise, and seismic inversion for anisotropic media. He is member of SEG.
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Wang, DL., Tong, ZF., Tang, C. et al. An iterative curvelet thresholding algorithm for seismic random noise attenuation. Appl. Geophys. 7, 315–324 (2010). https://doi.org/10.1007/s11770-010-0259-8
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DOI: https://doi.org/10.1007/s11770-010-0259-8