Research on seismic data denoising method based on K-SVD algorithm

Deshu Lin; Caifeng Cheng

doi:10.1117/12.3025559

28 February 2024 Research on seismic data denoising method based on K-SVD algorithm

Deshu Lin, Caifeng Cheng

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 1307135 (2024) https://doi.org/10.1117/12.3025559
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

In data processing, the removal of random noise is an essential step in order to improve quality. This article presents a detailed study on the removal of random noise in seismic signals, aiming to seek a more efficient and practical denoising method. This paper proposes a seismic data denoising method based on the K-SVD algorithm. By performing sparse decomposition and dictionary learning on seismic signals, the method achieves effective denoising of seismic signals. The results show that this method has a good denoising effect on seismic data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Deshu Lin and Caifeng Cheng "Research on seismic data denoising method based on K-SVD algorithm", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 1307135 (28 February 2024); https://doi.org/10.1117/12.3025559

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KEYWORDS

Associative arrays

Denoising

Machine learning

Signal processing

Matrices

Interference (communication)

Chemical species

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