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Seismic data denoising via shearlet transform and data‐driven tight frame
Author(s) -
Zhang Liang,
Tang Jingtian
Publication year - 2019
Publication title -
acta geologica sinica ‐ english edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 61
eISSN - 1755-6724
pISSN - 1000-9515
DOI - 10.1111/1755-6724.14105
Subject(s) - shearlet , thresholding , sort , noise reduction , computer science , sparse approximation , artificial intelligence , pattern recognition (psychology) , robustness (evolution) , frame (networking) , algorithm , image (mathematics) , information retrieval , telecommunications , biochemistry , chemistry , gene
We propose a sort of double sparsity dictionary (DSD) to deal with random noise of seismic exploration, which consists of shearlet transform and data‐driven tight frame (DDTF). We train the DDTF dictionary in the domain of shearlet transform to improve the robustness of the dictionary. Furthermore, the function of hard‐thresholding is applied to find dictionary coefficients and cut small shearlet coefficients. Finally, we verified the reliability of the proposed approach by a synthetic data denoising example.