Abstract
Traditional denoising methods based on fixed transforms are not suited for exploiting their complicated characteristics and attenuating noise due to their lack of adaptability. Recently, a novel method called morphological component analysis (MCA) was proposed to separate different geometrical components by amalgamating several irrelevance transforms. For studying the local singular and smooth linear components characteristics of seismic data, we propose a novel method that excels particularly in attenuating random and coherent noise while preserving effective signals. The proposed method, which combines MCA, dictionary learning (DL), and deep noise reduction consists of three steps: first, we separate the local singular and smooth linear components from the seismic signal using MCA. Second, we apply a DL method on these two components to suppress noise and obtain the denoised signal and noise. In the final step, we apply the DL method to the noise to obtain a little of the seismic signal. Afterwards, we integrate the two seismic signals to obtain the final denoised seismic signal. Numerical results indicate that the proposed method can effectively suppress the undesired noise, maximally preserve the information of geologic bodies and structures, and improve the signal-to-noise ratio (S/N) of the data. We also demonstrate the superior performance of this approach by comparing with other novel dictionaries such as discrete cosine transforms (DCTs), undecimated discrete wavelet transforms (UDWTs), or curvelet transforms. This algorithm provides new ideas for data processing to advance quality and S/N of seismic data.
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Acknowledgements
The research was supported by National Natural Science Foundation of China (No. 41672325, 41602334), National Key Research and Development Program of China (No. 2017YFC0601505), Key R & D projects of Sichuan Science and Technology Department of China (No. 2021YFG0257), Opening Fund of Geomathematics Key Laboratory of Sichuan Province (No. SCSXDZ201709), Leading talent training project of Neijiang Normal University under 2017[Liu Yi-He], Innovative Team Program of the Neijiang Normal University under 17TD03, Sichuan province academic and technical leader training funded projects under 13XSJS002, Foundation of Ph. D. Scientific Research of Neijiang Normal University under 2019[zhang shuang] and 2019[wang jiujiang]. We would like to thank Quanhai Wang for valuable suggestions and express our gratitude to the Geomathematics Key Laboratory of Sichuan Province and Key Laboratory of Earth Exploration and Information Techniques of Ministry of Education. The support of anonymous reviewers and the topic editor Dr. McNamara is highly appreciated.
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Guo, Y., Guo, S., Guo, K. et al. Seismic data denoising under the morphological component analysis framework by dictionary learning. Int J Earth Sci (Geol Rundsch) 110, 963–978 (2021). https://doi.org/10.1007/s00531-021-02001-3
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DOI: https://doi.org/10.1007/s00531-021-02001-3