LAUSR

To improve the denoising performance of seismic data contaminated with random and coherent noises, a hybrid denoising scheme is proposed in this paper. It aims to whiten the random noise and identify the coherent noise for the preserved or prominent seismic features. With the wavelet and curvelet basis functions in curvelets alternately, the hybrid denoising scheme utilises the representation of edges and singularities along curves. Then it adapts the wavelet-based higher-order correlative stacking denoising method from seismic exploration sequentially. In regard to interpretive seismic records of bedrock surface detection after the artificial backfill, the noisy data are significantly improved both in denoising and fidelity. To prove the advantage of the hybrid denoising scheme, a comparison of the performances between the different individual denoising methods, including the higher-order correlative stacking method, curvelets with wavelet and curvelet basis functions, has been discussed for the complex seismic records contaminated with different noises. Numerical case studies and a field data analysis have been used to show that the proposed hybrid denoising scheme is prominent for seismic data containing complex features than the individual denoising methods.