Journal Article

An effective approach to attenuate random noise based on compressive sensing and curvelet transform

Zu, Shaohuan, Chen, Yangkang, Cao, Siyuan, Liu, Wei

2016

Overview

Random noise attenuation is an important step in seismic data processing. In this paper, we propose a novel denoising approach based on compressive sensing and the curvelet transform. We formulate the random noise attenuation problem as an L1 norm regularized optimization problem. We propose to use the curvelet transform as the sparse transform in the optimization problem to regularize the sparse coefficients in order to separate signal and noise and to use the gradient projection for sparse reconstruction (GPSR) algorithm to solve the formulated optimization problem with an easy implementation and a fast convergence. We tested the performance of our proposed approach on both synthetic and field seismic data. Numerical results show that the proposed approach can effectively suppress the distortion near the edge of seismic events during the noise attenuation process and has high computational efficiency compared with the traditional curvelet thresholding and iterative soft thresholding based denoising methods. Besides, compared with f-x deconvolution, the proposed denoising method is capable of eliminating the random noise more effectively while preserving more useful signals.

• Publisher: IOP Publishing,Oxford University Press
• Subject: Attenuation / compressive sensing / curvelet transform / Data processing / deconvolution / gradient projection for sparse reconstruction / Mathematical models / Noise / Noise reduction / Optimization / Random noise / random noise attenuation / Seismic studies / signal preservation / Transforms