1. Fundamental Science on Radioactive Geology and Exploration Technology Laboratory, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. Shanxi Provincial Coal Geological Exploration, Geophysical Prospecting, Surveying and Mapping Institute, Jinzhong, Shanxi 030600, China; 3. School of Resources and Environment, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China
Abstract：Real seismic data are often non-uniformly sampled due to limits of field acquisition environment and terrain conditions.But conventional curvelet-based denoising cannot process non-uniform sampled and noisy data.We first introduce the non-uniform fast Fourier transform (NFFT) in the multi-scale and multi-directional curvelet transform,and construct the regularized inversion of operator that takes the uniformly sampled curvelet coefficients to non-uniformly data.Then we use linearized Bregman method for the inversion calculation,and adopt soft thresholds to remove noise of the curvelet coefficients in each iteration,and get the noise free uniform curvelet coefficients.Finally we perform the conventional inverse fast discrete curvelet transform (FDCT) and get denoised seismic data.Tests on synthetic and real data reveal that the proposed method can better suppress random noise when it interpolates non-uniformly sampled data to uniformly sampled data.
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