AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE026230102
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Non-local weighted structure tensor total variation model for seismic data denoising

Rui Li1 ,  Zengqiang Qiao2*
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1 School of Science, Xi’an Technological University, Xi’an, Shaanxi , China
2 School of Mathematics and Statistics, Ningxia University, Yinchuan, Ningxia , China
JSE, 026230102 https://doi.org/10.36922/JSE026230102
Received: 7 June 2026 | Revised: 19 July 2026 | Accepted: 28 July 2026 | Published online: 26 August 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Suppressing random noise is a critical component in seismic data processing, exerting a pivotal role in enhancing the signal-to-noise ratio and the overall quality of seismic data. To effectively attenuate noise and retain fine details, a novel non-local weighted structure tensor total variation (NLWSTV) model is presented for seismic data noise reduction. This model effectively combined the local structural regularity and non-local self-similarity characteristics of seismic data. Specifically, we developed an anisotropic weighted matrix that allocated different weights to the discrete gradients in the horizontal and vertical directions to accurately grasp local features of seismic data. In parallel, we integrated a non-local version of the structure tensor total variation model harmoniously with the anisotropic weighted matrix to thoroughly investigate non-local feature information across the entire seismic data. To efficiently tackle the NLWSTV model, we adopted an alternating direction method of multipliers-based optimization algorithm. Comparative experiments clearly showed the effectiveness and superiority of the presented approach, especially in eliminating random noise while better preserving the fine details of geological structures.

Keywords
Seismic data denoising
Random noise
Structure tensor total variation
Non-local regularization
Alternating direction multiplier method
Funding
This work was supported by Research and Practice on the Construction of Digital Textbooks for General Education Courses in Colleges and Universities Empowered by AI (grant no. 2024008), and Research on the Construction and Teaching Practice of AI-driven University Mathematics Smart Teaching Model (grant no. 25JBS08).
Conflict of interest
The authors declare no conflicts of interest.
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Journal of Seismic Exploration, Print ISSN: 0963-0651, Published by AccScience Publishing
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