Seismic data denoising based on principal component analysis and K⁃SVD

Noise significantly affects the quality of seismic signals. Developing effective denoising methods for seismic data is a challenging task in the field of seismic exploration. To address the limitations of the traditional K-singular value decomposition (K-SVD)denoising algorithm in processing seismic data，this paper proposes a comprehensive method combining principal component analysis (PCA)and K - SVD. First，PCA is employed to perform dimensionality reduction on seismic data，transforming high-dimensional seismic data into a lower-dimensional feature space. This can effectively extract the main features of seismic signals，thereby reducing data redundancy and computational complexity. Next，the sparse nature of seismic signals is captured by represen - ting signals as a sparse linear combination of basis vectors with the combination of PCA and K -SVD，which effectively removes noise. Finally，the effectiveness of three methods is compared on both simulated and real seismic datasets. The results of trial computation and experiments show that the seismic data denoising method based on PCA and K-SVD not only removes noise from seismic data but also preserves key features of the seismic signal. It significantly improves the signal-to-noise ratio (SNR)of the seismic data. Compared with the traditional K - SVD algorithm，the proposed method achieves better denoising performance with lower computational costs，providing a novel approach for seismic data denoising. © 2025 Editorial office of Oil Geophysical Prospecting. All rights reserved.