Denoising and reconstruction of 3D seismic data on a data-driven tight frame
CHEN Jie1, NIU Cong2, LI Yong1,3, HUANG Rao2, CHEN Lixin1, MA Zechuan1
1. School of Geophysics, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 2. CNOOC Research Institute Co. Ltd., Beijing 100027, China; 3. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Chengdu University of Technology), Chengdu, Sichuan 610059, China
Abstract：Constrained by economic and geological conditions and other factors,generally seismic data are undersampled and noises are serious,which severely impact data processing and interpretation. We studied how to denoise and reconstruct 3D seismic data on a data-driven tight frame (DDTF). The DDTF theory defines the learning dictionary to be a translation-invariant redundant wavelet tight frame. By further controlling the degree of freedom of the dictionary,it makes the DDTF algorithm have good robustness,and the fine features of seismic data can be better preserved by making use of perfect reconstruction on the wavelet tight frame. Model and real data have proved that the DDTF algorithm works well in denoising and reconstructing 3D synthetic seismic data with simple structures and real 3D seismic data with complex structures,but the computational efficiency is low and needs to be further improved. In addition,curvelet transform is less effective for denoising and reconstructing real data;the denoised and reconstructed results from block matched four-dimensional cooperative filtering are too smooth to preserve some structural features.
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