Curvelet阈值迭代法地震随机噪声压制

• Published in: 应用地球物理（英文版） Vol. 7; no. 4; pp. 315 - 324
• Main Authors: 王德利, 仝中飞, 唐晨, 朱恒
• Format: Journal Article
• Language: Chinese
• Published: College of Geo-Exploration Science & Technology, Jilin University, Changchun 130026, China%CNOOC Research Institute, Beijing 10027, China 2010
• Subjects: random noise attenuation; iterative thresholding; curvelet transform
• ISSN: 1672-7975; 1993-0658
• DOI: 10.1007/s11770-010-0259-8

P3; 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.